US20120058824A1 - Scalable real-time motion recognition - Google Patents
Scalable real-time motion recognition Download PDFInfo
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- US20120058824A1 US20120058824A1 US12/876,979 US87697910A US2012058824A1 US 20120058824 A1 US20120058824 A1 US 20120058824A1 US 87697910 A US87697910 A US 87697910A US 2012058824 A1 US2012058824 A1 US 2012058824A1
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/40—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment
- A63F13/42—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment by mapping the input signals into game commands, e.g. mapping the displacement of a stylus on a touch screen to the steering angle of a virtual vehicle
- A63F13/428—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment by mapping the input signals into game commands, e.g. mapping the displacement of a stylus on a touch screen to the steering angle of a virtual vehicle involving motion or position input signals, e.g. signals representing the rotation of an input controller or a player's arm motions sensed by accelerometers or gyroscopes
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/80—Special adaptations for executing a specific game genre or game mode
- A63F13/814—Musical performances, e.g. by evaluating the player's ability to follow a notation
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/20—Input arrangements for video game devices
- A63F13/21—Input arrangements for video game devices characterised by their sensors, purposes or types
- A63F13/213—Input arrangements for video game devices characterised by their sensors, purposes or types comprising photodetecting means, e.g. cameras, photodiodes or infrared cells
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/20—Input arrangements for video game devices
- A63F13/21—Input arrangements for video game devices characterised by their sensors, purposes or types
- A63F13/215—Input arrangements for video game devices characterised by their sensors, purposes or types comprising means for detecting acoustic signals, e.g. using a microphone
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/80—Special adaptations for executing a specific game genre or game mode
- A63F13/833—Hand-to-hand fighting, e.g. martial arts competition
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/10—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals
- A63F2300/1087—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals comprising photodetecting means, e.g. a camera
- A63F2300/1093—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by input arrangements for converting player-generated signals into game device control signals comprising photodetecting means, e.g. a camera using visible light
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
- A63F2300/55—Details of game data or player data management
- A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
- A63F2300/5553—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history user representation in the game field, e.g. avatar
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/60—Methods for processing data by generating or executing the game program
- A63F2300/6045—Methods for processing data by generating or executing the game program for mapping control signals received from the input arrangement into game commands
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/60—Methods for processing data by generating or executing the game program
- A63F2300/66—Methods for processing data by generating or executing the game program for rendering three dimensional images
- A63F2300/6607—Methods for processing data by generating or executing the game program for rendering three dimensional images for animating game characters, e.g. skeleton kinematics
Definitions
- computing applications such as computer games and multimedia applications used controls to allow users to manipulate game characters or other aspects of an application.
- controls are input using, for example, controllers, remotes, keyboards, mice, or the like.
- computer games and multimedia applications have begun employing cameras and software gesture recognition engines to provide a human computer interface (“HCI”) or natural user interface (“NUI”).
- HCI human computer interface
- NUI natural user interface
- user motions are detected, and some motions or poses represent gestures which are used to control game characters (e.g., a user's avatar) or other aspects of a multimedia application.
- an image capture device captures images of the user's motions in its field of view.
- the field of view can be represented as a finite Euclidean three-dimensional (3-D) space.
- a user can be performing a gesture with a body part but the user also tends to move around in the field of view of the capture device.
- a person extending her arm as a gesture while dancing about the room will generate a motion trajectory that will require more processing to decouple the arm action from the effect of the dancing motion.
- users perform gestures at different speeds, providing another factor for which to account in gesture determination.
- the gesture recognition and subsequent responsive action need to be done in real-time and processed within the frame rate of the NUI system.
- the skeletal model represents the human body as a model of joints and bones and having a rigid body portion.
- Sets of skeletal data representing motion in terms of the human skeletal model are received which are defined in a camera-based three-dimensional (3-D) coordinate reference system.
- a camera-based 3-D reference system has a frame of reference defined with respect to an origin in a camera's field of view.
- the camera-based 3-D reference system may be spatially transformed to a body-based coordinate 3-D reference system having a frame of reference defined with respect to a position within the rigid body portion of the skeletal model.
- the body-based coordinate 3-D reference system is independent of the camera's field of view.
- the system determines motion of at least one body part using the rigid body-based 3-D reference system. Furthermore, gesture recognition is performed based on motion represented with respect to the body-based 3-D reference system.
- FIG. 1C illustrates an example of a visually distinctive indicator providing feedback to a user on his performance.
- FIG. 2 illustrates an example embodiment of a capture device that may be used in an embodiment of a target recognition, analysis, and tracking system.
- FIG. 3A illustrates an example embodiment of a computing environment that may be used to recognize human body motion in a target recognition, analysis, and tracking system.
- FIG. 3B illustrates another example embodiment of a computing environment that may be used to recognize human body motion in a target recognition, analysis, and tracking system.
- FIG. 5 is a flowchart of an embodiment of a method for scalable real-time gesture recognition.
- FIG. 7 illustrates an example of a skeletal model including a rigid body portion that can be used by one or more embodiments.
- FIG. 9 illustrates an example of a camera-based 3-D reference system superimposed with a body-based 3-D reference system with three angles for transforming between them.
- FIG. 12B illustrates an example of a 3-D orthogonal coordinate system centered at a first degree joint which is used for determining motion of a second degree joint attached to a common bone.
- FIG. 14 shows a block diagram of a gesture recognition engine embodiment.
- FIG. 15 shows a flowchart of the operation of the gesture recognition engine of FIG. 14 .
- the hardware for implementing the present technology includes a target recognition, analysis, and tracking system 10 which may be used to recognize, analyze, and/or track a human target such as the user 18 .
- Embodiments of the target recognition, analysis, and tracking system 10 include a computing environment 12 for executing a gaming or other application, and an audiovisual device 16 for providing audio and visual representations from the gaming or other application.
- the system 10 further includes a capture device 20 for capturing positions and movements performed by the user, which the computing environment receives, interprets and uses to control the gaming or other application.
- the application executing on the computing environment 12 may be a game with real time interaction such as a boxing game that the user 18 may be playing.
- the computing environment 12 may use the audiovisual device 16 to provide a visual representation of a boxing opponent 19 to the user 18 .
- the computing environment 12 may also use the audiovisual device 16 to provide a visual representation of a player avatar 21 that the user 18 may control with his or her movements.
- the user 18 may throw a punch in physical space to cause the player avatar 21 to throw a punch in game space.
- the computer environment 12 and the capture device 20 of the target recognition, analysis, and tracking system 10 may be used to recognize and analyze the punch of the user 18 in physical space such that the punch may be interpreted as a game control of the player avatar 21 in game space.
- FIG. 1B illustrates embodiments of a camera field of view 17 and a 3-D orthogonal coordinate reference system defined within the field of view.
- user 18 is interacting with a dance exercise application in which the user is dancing to music.
- the movements of user 18 are captured by the capture device 20 , which, in conjunction with computing environment 12 , animates and controls the movements of an avatar on a display just like in the boxing example. Some of his movements may be gestures.
- the origin of a 3-D orthogonal coordinate reference system is depicted in the center of the camera's 20 field of view which is located between the user 18 and his arm chair 23 .
- a skeletal model as discussed below is derived from each captured image frame, and initially the skeletal model is represented in this camera-based coordinate system.
- This coordinate system is called camera-based because the position of the camera determines the field of view and the space is characterized using planes and normals defined with respect to the camera.
- the camera-based reference system is fixed. It does not move with the user.
- FIG. 1C illustrates an example of a visually distinctive indicator 11 displayed to a user on the user's avatar 21 on audiovisual device 16 providing feedback to the user 18 on his performance. His right leg of avatar 21 is highlighted 11 on the audiovisual device 16 to provide visual feedback that the user's leg lift with his right leg is not meeting a standard, for example height, of a leg lift gesture of a model or the average performed by others using the dance exercise game.
- FIG. 2 illustrates an example embodiment of the capture device 20 that may be used in the target recognition, analysis, and tracking system 10 . Further details relating to a capture device for use with the present technology are set forth in one or more of the above co-pending patent applications.
- the capture device 20 may be configured to capture video having a depth image that may include depth values via any suitable technique including, for example, time-of-flight, structured light, stereo image, or the like.
- the capture device 20 may organize the calculated depth information into “Z layers,” or layers that may be perpendicular to a Z axis extending from the depth camera along its line of sight.
- the image camera component 22 may include an IR light component 24 , a three-dimensional (3-D) camera 26 , and an RGB camera 28 that may be used to capture the depth image of a scene.
- the IR light component 24 of the capture device 20 may emit an infrared light onto the scene and may then use sensors (not shown) to detect the backscattered light from the surface of one or more targets and objects in the scene using, for example, the 3-D camera 26 and/or the RGB camera 28 .
- the capture device 20 may include two or more physically separated cameras that may view a scene from different angles, to obtain visual stereo data that may be resolved to generate depth information.
- the capture device 20 may further include a microphone 30 .
- the microphone 30 may include a transducer or sensor that may receive and convert sound into an electrical signal. According to one embodiment, the microphone 30 may be used to reduce feedback between the capture device 20 and the computing environment 12 in the target recognition, analysis, and tracking system 10 . Additionally, the microphone 30 may be used to receive audio signals that may also be provided by the user to control applications such as game applications, non-game applications, or the like that may be executed by the computing environment 12 .
- the capture device 20 may further include a processor 32 that may be in operative communication with the image camera component 22 .
- the processor 32 may include a standardized processor, a specialized processor, a microprocessor, or the like that may execute instructions for receiving the depth image, determining whether a suitable target may be included in the depth image, converting the suitable target into a skeletal representation or model of the target, or any other suitable instruction.
- the capture device 20 may further include a memory component 34 that may store the instructions that may be executed by the processor 32 , images or frames of images captured by the 3-D camera or RGB camera, or any other suitable information, images, or the like.
- the memory component 34 may include random access memory (RAM), read only memory (ROM), cache, Flash memory, a hard disk, or any other suitable storage component.
- RAM random access memory
- ROM read only memory
- cache Flash memory
- hard disk or any other suitable storage component.
- the memory component 34 may be a separate component in communication with the image camera component 22 and the processor 32 .
- the memory component 34 may be integrated into the processor 32 and/or the image camera component 22 .
- the capture device 20 may be in communication with the computing environment 12 via a communication link 36 .
- the communication link 36 may be a wired connection including, for example, a USB connection, a Firewire connection, an Ethernet cable connection, or the like and/or a wireless connection such as a wireless 802.11b, g, a, or n connection.
- the computing environment 12 may provide a clock to the capture device 20 that may be used to determine when to capture, for example, a scene via the communication link 36 .
- the capture device 20 may provide the depth information and images captured by, for example, the 3-D camera 26 and/or the RGB camera 28 , and a skeletal model that may be generated by the capture device 20 to the computing environment 12 via the communication link 36 .
- Skeletal mapping techniques may then be used to determine various spots on that user's skeleton, joints of the hands, wrists, elbows, knees, neck, ankles, shoulders, and where the pelvis meets the spine.
- Other techniques include transforming the image into a body model representation of the person and transforming the image into a mesh model representation of the person.
- the skeletal model may then be provided to the computing environment 12 such that the computing environment may track the skeletal model and render an avatar associated with the skeletal model.
- the computing environment may further determine which controls to perform in an application executing on the computer environment based on, for example, gestures of the user that have been recognized from the skeletal model.
- the computing environment 12 may include a gesture recognition engine 190 .
- the gesture recognition engine 190 is explained hereinafter, but may in general include a collection of gesture filters, each comprising information concerning a gesture that may be performed by the skeletal model (as the user moves).
- the data captured by the cameras 26 , 28 and device 20 in the form of the skeletal model and movements associated with it may be compared to the gesture filters in the gesture recognition engine 190 to identify when a user (as represented by the skeletal model) has performed one or more gestures. Those gestures may be associated with various controls of an application. Thus, the computing environment 12 may use the gesture recognition engine 190 to interpret movements of the skeletal model and to control an application based on the movements.
- FIG. 3A illustrates an example embodiment of a computing environment that may be used to interpret one or more gestures in a target recognition, analysis, and tracking system.
- the computing environment such as the computing environment 12 described above with respect to FIGS. 1A-2 may be a multimedia console 100 , such as a gaming console.
- the multimedia console 100 has a central processing unit (CPU) 101 having a level 1 cache 102 , a level 2 cache 104 , and a flash ROM 106 .
- the level 1 cache 102 and a level 2 cache 104 temporarily store data and hence reduce the number of memory access cycles, thereby improving processing speed and throughput.
- the CPU 101 may be provided having more than one core, and thus, additional level 1 and level 2 caches 102 and 104 .
- the flash ROM 106 may store executable code that is loaded during an initial phase of a boot process when the multimedia console 100 is powered ON.
- a graphics processing unit (GPU) 108 and a video encoder/video codec (coder/decoder) 114 form a video processing pipeline for high speed and high resolution graphics processing. Data is carried from the GPU 108 to the video encoder/video codec 114 via a bus. The video processing pipeline outputs data to an A/V (audio/video) port 140 for transmission to a television or other display.
- a memory controller 110 is connected to the GPU 108 to facilitate processor access to various types of memory 112 , such as, but not limited to, a RAM.
- the multimedia console 100 includes an I/O controller 120 , a system management controller 122 , an audio processing unit 123 , a network interface controller 124 , a first USB host controller 126 , a second USB host controller 128 and a front panel I/O subassembly 130 that are preferably implemented on a module 118 .
- the USB controllers 126 and 128 serve as hosts for peripheral controllers 142 ( 1 )- 142 ( 2 ), a wireless adapter 148 , and an external memory device 146 (e.g., flash memory, external CD/DVD ROM drive, removable media, etc.).
- the network interface 124 and/or wireless adapter 148 provide access to a network (e.g., the Internet, home network, etc.) and may be any of a wide variety of various wired or wireless adapter components including an Ethernet card, a modem, a Bluetooth module, a cable modem, and the like.
- a network e.g., the Internet, home network, etc.
- wired or wireless adapter components including an Ethernet card, a modem, a Bluetooth module, a cable modem, and the like.
- the front panel I/O subassembly 130 supports the functionality of the power button 150 and the eject button 152 , as well as any LEDs (light emitting diodes) or other indicators exposed on the outer surface of the multimedia console 100 .
- a system power supply module 136 provides power to the components of the multimedia console 100 .
- a fan 138 cools the circuitry within the multimedia console 100 .
- the CPU 101 , GPU 108 , memory controller 110 , and various other components within the multimedia console 100 are interconnected via one or more buses, including serial and parallel buses, a memory bus, a peripheral bus, and a processor or local bus using any of a variety of bus architectures.
- bus architectures can include a Peripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.
- application data may be loaded from the system memory 143 into memory 112 and/or caches 102 , 104 and executed on the CPU 101 .
- the application may present a graphical user interface that provides a consistent user experience when navigating to different media types available on the multimedia console 100 .
- applications and/or other media contained within the media drive 144 may be launched or played from the media drive 144 to provide additional functionalities to the multimedia console 100 .
- the memory reservation preferably is large enough to contain the launch kernel, concurrent system applications and drivers.
- the CPU reservation is preferably constant such that if the reserved CPU usage is not used by the system applications, an idle thread will consume any unused cycles.
- lightweight messages generated by the system applications are displayed by using a GPU interrupt to schedule code to render popup into an overlay.
- the amount of memory required for an overlay depends on the overlay area size and the overlay preferably scales with screen resolution. Where a full user interface is used by the concurrent system application, it is preferable to use a resolution independent of the application resolution. A scaler may be used to set this resolution such that the need to change frequency and cause a TV resynch is eliminated.
- the multimedia console 100 boots and system resources are reserved, concurrent system applications execute to provide system functionalities.
- the system functionalities are encapsulated in a set of system applications that execute within the reserved system resources described above.
- the operating system kernel identifies threads that are system application threads versus gaming application threads.
- the system applications are preferably scheduled to run on the CPU 101 at predetermined times and intervals in order to provide a consistent system resource view to the application. The scheduling is to minimize cache disruption for the gaming application running on the console.
- a multimedia console application manager controls the gaming application audio level (e.g., mute, attenuate) when system applications are active.
- Input devices are shared by gaming applications and system applications.
- the input devices are not reserved resources, but are to be switched between system applications and the gaming application such that each will have a focus of the device.
- the application manager preferably controls the switching of input stream, without knowledge of the gaming application's knowledge and a driver maintains state information regarding focus switches.
- the cameras 26 , 28 and capture device 20 may define additional input devices for the console 100 .
- FIG. 3B illustrates another example embodiment of a computing environment 220 that may be the computing environment 12 shown in FIGS. 1A-2 used to interpret one or more gestures in a target recognition, analysis, and tracking system.
- the computing system environment 220 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the presently disclosed subject matter. Neither should the computing environment 220 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 220 .
- the various depicted computing elements may include circuitry configured to instantiate specific aspects of the present disclosure.
- the term circuitry used in the disclosure can include specialized hardware components configured to perform function(s) by firmware or switches.
- circuitry can include a general purpose processing unit, memory, etc., configured by software instructions that embody logic operable to perform function(s).
- an implementer may write source code embodying logic and the source code can be compiled into machine readable code that can be processed by the general purpose processing unit. Since one skilled in the art can appreciate that the state of the art has evolved to a point where there is little difference between hardware, software, or a combination of hardware/software, the selection of hardware versus software to effectuate specific functions is a design choice left to an implementer.
- the computing environment 220 comprises a computer 241 , which typically includes a variety of computer readable media.
- Computer readable media can be any available media that can be accessed by computer 241 and includes both volatile and nonvolatile media, removable and non-removable media.
- the system memory 222 includes computer storage media in the form of volatile and/or nonvolatile memory such as ROM 223 and RAM 260 .
- BIOS basic input/output system
- RAM 260 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 259 .
- FIG. 3B illustrates operating system 225 , application programs 226 , other program modules 227 , and program data 228 .
- the computer 241 may also include other removable/non-removable, volatile/nonvolatile computer storage media.
- FIG. 3B illustrates a hard disk drive 238 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 239 that reads from or writes to a removable, nonvolatile magnetic disk 254 , and an optical disk drive 240 that reads from or writes to a removable, nonvolatile optical disk 253 such as a CD ROM or other optical media.
- removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like.
- the hard disk drive 238 is typically connected to the system bus 221 through a non-removable memory interface such as interface 234
- magnetic disk drive 239 and optical disk drive 240 are typically connected to the system bus 221 by a removable memory interface, such as interface 235 .
- the drives and their associated computer storage media discussed above and illustrated in FIG. 3B provide storage of computer readable instructions, data structures, program modules and other data for the computer 241 .
- hard disk drive 238 is illustrated as storing operating system 258 , application programs 257 , other program modules 256 , and program data 255 .
- operating system 258 application programs 257 , other program modules 256 , and program data 255 are given different numbers here to illustrate that, at a minimum, they are different copies.
- a user may enter commands and information into the computer 241 through input devices such as a keyboard 251 and a pointing device 252 , commonly referred to as a mouse, trackball or touch pad.
- Other input devices may include a microphone, joystick, game pad, satellite dish, scanner, or the like.
- These and other input devices are often connected to the processing unit 259 through a user input interface 236 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB).
- the cameras 26 , 28 and capture device 20 may define additional input devices for the console 100 .
- a monitor 242 or other type of display device is also connected to the system bus 221 via an interface, such as a video interface 232 .
- computers may also include other peripheral output devices such as speakers 244 and printer 243 , which may be connected through an output peripheral interface 233 .
- the computer 241 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 246 .
- the remote computer 246 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 241 , although only a memory storage device 247 has been illustrated in FIG. 3B .
- the logical connections depicted in FIG. 3B include a local area network (LAN) 245 and a wide area network (WAN) 249 , but may also include other networks.
- LAN local area network
- WAN wide area network
- Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.
- the computer 241 When used in a LAN networking environment, the computer 241 is connected to the LAN 245 through a network interface or adapter 237 . When used in a WAN networking environment, the computer 241 typically includes a modem 250 or other means for establishing communications over the WAN 249 , such as the Internet.
- the modem 250 which may be internal or external, may be connected to the system bus 221 via the user input interface 236 , or other appropriate mechanism.
- program modules depicted relative to the computer 241 may be stored in the remote memory storage device.
- FIG. 3B illustrates remote application programs 248 as residing on memory device 247 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
- FIG. 4A depicts an example skeletal mapping of a user that may be generated from the capture device 20 .
- a variety of joints and bones are identified: each hand 402 , each forearm 404 , each elbow 406 , each bicep 408 , each shoulder 410 , each hip 412 , each thigh 414 , each knee 416 , each foreleg 418 , each foot 420 , the head 422 , the mid spine 424 , the top 426 and the bottom 428 of the spine, and the waist 430 .
- additional features may be identified, such as the bones and joints of the fingers or toes, or individual features of the face, such as the nose and eyes. It is understood that one or more of the points shown in FIG. 4A may be omitted and/or others may be added.
- a skeletal mapping is one example of a computer model of a user, and other computer models are contemplated.
- Each of the points in FIG. 4A may be described in 3-dimensional Cartesian space by an x, y and z coordinate in a frame of reference with respect to the capture device 20 (camera space).
- any absolute motion of a joint in this reference frame needs to be computed.
- FIG. 1B if the user 18 is moving in the frame of reference with his hand 18 a straight down and stationary with respect to his body, that hand is nonetheless moving in camera space frame of reference. Time and processor resources are required to determine its movement in camera space relative to a prior frame.
- an upstream joint is the next adjacent joint closer to the torso. So the upstream joint of the wrist is the elbow, and the upstream joint of the elbow is the shoulder; the upstream joint of the ankle is the knee, and the upstream joint of the knee is the hip.
- Rigid-body transformation e.g., translation and rotation
- the camera frame of reference provides the same information as to joint position, but does so in more efficient and low entropy manner.
- the user's hand is not moving relative to its upstream joint.
- tracking the user's hand in body space simplifies joint tracking from frame to frame.
- tracking movement of joints relative to other joints results in smaller search space and data set, and faster processing and gesture recognition as explained below. It is a representation which is invariant to the group of similarity transformations (scaling, rotation, translation) in 3D.
- Another aspect of the present technology is to treat the torso, including the shoulders and hips, as a rigid body. This good approximation allows the torso to be described with three angles, described below, relative to camera space, simplifying skeletal tracking
- the method may be performed by software executing on one or more processors such as the processor 32 of capture device 20 , the CPU 101 ( FIG. 3A ) or the processing unit 299 ( FIG. 3B ) of the computing environment 12 for a target recognition, analysis tracking and tracking system 10 .
- processors such as the processor 32 of capture device 20 , the CPU 101 ( FIG. 3A ) or the processing unit 299 ( FIG. 3B ) of the computing environment 12 for a target recognition, analysis tracking and tracking system 10 .
- Frames of skeletal model data are received in step 502 from capture device 20 representing a human body in a three-dimensional space with respect to the fixed camera-based 3-D coordinate reference system.
- the size of bones in the sets of skeletal data are normalized 504 .
- different users may be of different sizes, with limbs and torsos of different sizes.
- the distances measured between joints for a given user are normalized to a standard length for such distances. This can be done on the received skeletal model data or on the scaled skeletal data sets.
- the normalization of the size of bones in step 506 may be omitted.
- One goal of the present system is to identify movements, such as dance movements of a user when dancing to music played by an application running on computing environment 12 .
- the present system makes use of the fact that movements, such as dance movements, are typically repetitive. There are basic movements at each beat of the music, with a combination of these basic movements forming a multi-beat motion that itself repeats. Thus, a user may repeat a given movement once per beat of music, or sets of beats. As the music speeds up, the user tends to move faster. As the music slows down, the user tends to move slower. The result is that the movements a user tends to make repeat every beat, or predefined number of beats. Accordingly, the present system analyzes repetitive movements over a period not based in time, but rather based on the beat of the music (or other periodic unit of measurement).
- step 506 software executing in the system normalizes the number of frames of skeletal data to a periodic unit of measurement to provide normalized skeletal data sets.
- a periodic unit of measure is a predefined number of beats in music.
- the present system is able to normalize repetitive user movements to a fixed period, independent of time. For music having a faster beat, the number of frames in the period over which a user completes one cycle of movement will be faster. For music having a slower beat, the number of frames in the period over which a user completes a cycle of movement will be slower. However, the period itself is independent of time.
- the beat of the music and how it changes in a piece is predetermined generally, but it can be detected as well using music software if necessary.
- beats as a reference, rather than time, gestures can be recognized independently of the speed at which they are made. Normalizing the number of frames to a beat or other period simplifies calculations in real-time gesture recognition by making it easier to identify repetitive movements within repetitive fixed periods. This information may for example be used to identify gestures or specific movements, such as dance movements, as explained below with respect to FIGS. 14 and 15 .
- FIGS. 6A and 6B illustrate an example of a captured set of skeletal data frames derived from received frames of images captured by the capture device 20 , referred to as skeletal data frames, and a scaled set of skeletal data frames respectively.
- the periodic unit is a beat
- the number of repeating units is 8 beats for a repeating period, called a beat period in this example.
- a number of frames is determined.
- the number of frames can be the number of frames captured in a repeating period based on the frame rate of the capture device 20 .
- the beat period is 8 beats
- the normalized number of frames for the beat period is 120.
- the start of the repeating period is synchronized with the start of the frame capture.
- a frame is interpolated for each dropped frame.
- data for a frame can be interpolated from the frames which came before and after the dropped frame.
- a frame typically has a time stamp which indicates its place in a succession of frames.
- interpolated frame 604 i is generated based on the skeleton data of frames 602 and 606
- interpolated frame 614 i is generated based on the skeleton data of frames 612 and 616 .
- an elbow joint may have moved from one position in three-dimensional space in frame 602 to another in frame 606 .
- Interpolated frame 604 i may represent a halfway point for the elbow joint between the two positions.
- missing frames are interpolated to produce the exemplar set of scaled skeletal data.
- the present system captures information relating to the position of a skeleton, over the predefined repeating period discussed above, and determines whether the user is performing a known movement over that period.
- the frame of reference is transformed to a body-based frame of references, where movement of body parts relative to each other, and not the field of view as a whole, are considered.
- positions of joints relative to each other are measured for each frame. This position and angle information may then be compared against stored information to identify whether the user is performing some known movement.
- step 508 software executing in the capture device 20 or the computing environment 12 or both generates a body-based coordinate 3-D reference system having a frame of reference defined with respect to a position within a rigid body portion of a skeletal model.
- the present technology models a group of joints as a single rigid body 432 .
- the joints in the rigid body 432 include the neck joint 426 , the two shoulder joints 410 a and 410 b, the mid-spine 424 and lower spine 428 joints, and the hip joints 412 a and 412 b. It is understood that the rigid body portion 432 may include fewer joints, or additional joints, in further embodiments.
- the joints in the rigid body have little or no relative motion with respect to each other and, as such, are treated as a single rigid body by the present technology. Joints within the torso in reality do move, but the motion is to a much lesser extent than for a knee or a hand.
- the rigid body portion 432 shown in the model of FIG. 7 can be treated as a single block representing the user's body position in the camera-based 3-D coordinate system for the camera's field of view 17 . If the user 18 moves left or right, forward or backward, his torso does as well and can be treated as a single unit, or rigid body. Torso data is robust in that data detected for the torso has a high probability of accuracy even when the data is very noisy.
- PCA principal component analysis
- FIG. 8 illustrates an example of a fixed camera-based 3-D reference system 5 and an example of a body-based 3-D reference system 800 .
- the a-axis is the axis of maximum variability
- the length of the spine in FIG. 4B the b-axis the axis of next most variability
- the axis from shoulder to shoulder the c-axis the cross product of axes a and b, a ray out from the chest of a user.
- the orientation of the a, b, c system 800 varies within the camera-based reference system 5 .
- step 508 the system 10 performs PCA on data points, the shoulders, neck, spine joints, and hip joints (seven joints), of the rigid body portion 432 of the skeletal model 400 to obtain axes of an orthogonal coordinate system defined with respect to the rigid body portion.
- the capture device 20 or the computing environment 12 or both together return a skeletal data set representing the position of the user in the body-based reference frame.
- FIGS. 7 and 8 show examples of a body-based 3-D coordinate reference system embodiment 800 having an origin (0,0,0) at the neck 426 (labeled in FIG. 7 ), with an a-axis extending along the spine, a b-axis extending across the shoulders and orthogonal to the a-axis, and a c-axis orthogonal to the plane formed by the a and b axes and representing motion in and out of that plane.
- the positive c-axis extends from the neck out of the page. It is understood that the origin may be positioned at locations within the rigid body 432 other than the neck joint 426 .
- the vectors a, b and c determined by PCA will be based on the user position relative to the camera. Computing the vectors a, b and c over the torso by PCA provides an orientation of the torso with respect to Cartesian space. Depending on where the capture device 20 is, the vectors a, b and/or c will have a different orientation.
- the next step ( 510 ) in the process involves describing the rigid body-based vectors a, b and c in terms of the camera-based 3-D coordinate system for each frame of the scaled skeletal data. While this may be done by a variety of methods, in one example explained below, Euler angles may be used for this transformation. In one example, the type of Euler angles used are pitch, yaw and roll, also known as Tait-Bryan angles. Other types of Euler angles may be used in further embodiments. Another possible method for this transformation beside Euler angles is a direction of cosines approach.
- FIG. 9 illustrates the coordinate systems 5 and 800 superimposed on each other with three Euler angles for transforming between them for a given skeletal data frame.
- Euler angles can be used to represent the spatial orientation of any coordinate system, such as that for a rigid body vectors a, b, c, as a composition of rotations from a frame of reference coordinate system, such as the camera-based reference system x, y, z.
- the intersection of the xy and the be coordinate planes is vector (N) which is a line perpendicular to both the z and a-axis, sometimes referred to as the line of nodes.
- Alpha a is the angle between the x-axis and the vector N.
- Beta ⁇ is the angle between the z-axis and the a-axis.
- Gamma ⁇ is the angle between the vector N and the c-axis. In other examples, the angles could be between other axes pairings just as long as the transformation angles represent consistent pairings.
- a composition of rotations is composing three rotations, each around a single axis.
- Euler angles can be used to represent the relative orientation of the body based coordinate system to the camera-based coordinate system.
- Euler rotations are defined as the movement obtained by changing one of the Euler angles while leaving the other two constant.
- Euler rotations are expressed in terms of the camera-based coordinate system, the body based coordinate system and intermediate coordinate reference systems. There are also a number of valid known sequences of rotations. The axis selected for the start of rotation is arbitrary, but it cannot be used twice in succession.
- the first angle moves the line of nodes around the fixed axis z
- the second rotates around the vector N
- the third one is an intrinsic rotation around an axis fixed in the body that moves, in this example axis c.
- transformations between camera-based 3-D space and body-based 3-D space may be made by a variety of other known transformation matrices and equations.
- orientation matrix which includes, in its three columns, the Cartesian coordinates of three points. These points are used to define the orientation of the axes of the local system; they are the tips of three unit vectors aligned with those axes.
- the three Euler angles are transformation angles which can be used to represent the position of the rigid body torso in terms of the camera-based orthogonal 3-D coordinate system 5 in order to determine motion of the torso. For example, this determination can tell if a user is standing sideways or is twisting his torso.
- the software executing on a processor in the target recognition, analysis, and tracking system 10 determines a Euler transformation angle ⁇ , ⁇ , ⁇ between each axis of the rigid body coordinate system and an axis of the fixed camera-based coordinate system, and these three angles form part of a motion determination data set for each scaled data set.
- the next step ( 512 ) is to describe the positions of first degree and second degree joints extending from the torso.
- the absolute motion of the joint for example an elbow, can be represented in terms of the relative motion of the joint with respect to the torso, and the motion of torso with respect to the camera-based 3-D reference system.
- the first degree joints are those which are one joint away from the rigid torso (elbow joints 406 and knee joints 416 ), as indicated by circles in FIG. 7 . These first degree joints may be thought of as being anchored to the rigid body by a constant bone length.
- the second degree joints are those which are one joint away from the first degree joints (wrist joints 402 and ankle joints 420 ), as indicated by squares in FIG. 7 . These second degree joints may be thought of as being anchored to the first degree joints by a constant bone length.
- the skeletal model of FIG. 7 can be thought of as a tree structure in which the rigid body torso is the base of the tree, and the user's arms, legs and head forming branches extending from the base of the tree.
- FIG. 10 illustrates an example in which a body-based 3-D orthogonal coordinate system of a skeletal model including a rigid body portion is translated from the origin within the rigid body to another joint within the rigid body.
- the joint in the rigid body to which it is attached is stationary and can be treated as an origin of a coordinate reference system.
- the origin is translated from the neck joint 426 along the b-axis to the right shoulder 410 b so that the position of the left elbow 406 b may be calculated relative to the left shoulder.
- the distance from neck to right shoulder is known.
- the joints are deemed stationary and separated by known, normalized distances between joints.
- a translation of the reference system having its origin at the neck can be linearly translated to another joint in the rigid body.
- the origin at neck joint 426 is translated along the b-axis to the right shoulder 410 b.
- the present system treats the same body parts in human bodies as connected in a known, tree-like relation.
- the right elbow 406 b is connected to the right shoulder 410 b via an upper arm bone 408 b.
- This known connectedness in addition to the bone sizes being normalized allows the system to predetermine the translation of the origin of the a, b, c vector reference system for the different joints of the skeleton.
- FIG. 11 is a flowchart providing further detail of step 512 for representing motion of a joint in the set of first degree joints attached to the rigid body portion.
- software executing on a processor of a target recognition, tracking and analysis system translates 1002 the frame of reference for the 3-D orthogonal body-based coordinate system from an origin of the system on the rigid body to a joint of the rigid body to which an end of a bone attached to the respective first degree joint is attached.
- the position of the first degree joint is determined 1004 using spherical coordinates with respect to the body-based 3-D reference system.
- a point in 3-D space can be represented by a radial distance r from a fixed origin, an inclination or zenith angle ⁇ measured from a fixed zenith direction, and an azimuth angle ⁇ of its orthogonal projection on a reference plane that passes through the origin and is orthogonal to the zenith.
- the azimuth angle is measured from a fixed reference axis on the plane.
- the upper arm bone 408 b between the right shoulder 410 b and right elbow 406 b is the radial distance. This is a constant, so the system need not calculate or store its length for each set of the scaled data.
- the a-axis acts as the zenith or spherical north which forms the zenith angle or inclination angle theta ⁇ . This angle theta ⁇ is measured between the a-axis and the upper arm bone 408 b.
- Axes b and c form a reference plane passing through the origin with a and orthogonal to a as zenith in which the perpendicular projection r′ of the upper arm 408 b is projected.
- the azimuth angle ⁇ b is measured from the b-axis to the projection r′ and the azimuth angle ⁇ c is measured from the c-axis to the projection r′. Due to being in the same plane and being measured from fixed reference axes, ⁇ with respect to one planar axis can be determined from ⁇ with respect to the other axis.
- ⁇ c ⁇ /2 ⁇ b. However, if ⁇ /2 ⁇ b,c ⁇ /2, then the information from ⁇ b and ⁇ c is not redundant. Thus, ⁇ may be stored, and, in different embodiments, one or both angles ⁇ b and ⁇ c may be stored.
- the zenith angle theta ⁇ and the azimuth angle ⁇ referenced with respect to one of the axes, b or c, defining the orthogonal reference plane in which the perpendicular projection of the bone is made, ⁇ b or ⁇ c, are stored 1006 in a motion determination data set for the first degree joint in one of the sets of scaled skeletal data
- FIG. 12A illustrates an example of another transformation of a body-based 3-D coordinate system having an origin in the rigid body portion to at a first degree joint in a set of the scaled skeletal data for determining motion of a second degree joint.
- the foot joint 420 a is in the set of second degree joints in which each respective joint is attached to a bone 418 a, another end of which is attached to one of the joints in the set of first degree joints, in this case knee 416 a.
- the origin can be translated into the first degree knee joint 416 a which is attached via a bone 414 a to the hip joint 412 a within the rigid body torso 432 .
- the knee 416 a moves around, but the rigid body torso based 3-D coordinate reference system provides robust or accurate data with respect to the axis c coming out of the chest of the skeletal model. Additionally, the bone 414 a between the hip 412 a and the knee 416 a are very reliable and the foot 420 a is always attached to the knee 416 a. It is also constant size due to the normalization.
- the bone 414 a between the hip and knee is extended out the other side of the knee and acts as a zenith or spherical north from which a zenith or inclination angle theta ⁇ can be defined with the shin bone 418 a between the knee 416 a and the foot 420 a.
- the bone extension vector 1102 is not necessarily orthogonal to the c vector of the torso transformed coordinate system 800 of axes a,b and c.
- FIG. 12B illustrates an example of a 3-D orthogonal coordinate system centered at a first degree joint which is used for determining motion of a second degree joint attached to a common bone.
- An orthogonal 3-D coordinate system is generated with the knee 416 a as origin and the bone extension 1102 as the zenith axis.
- the cross product of the c vector and the bone vector 1102 results in vector P, and another orthogonal vector Q is generated by crossing the bone vector 1102 with P.
- FIG. 12B represents the relationship between the second degree joint, foot 420 a, and a first degree joint, knee 416 a, using another 3-D coordinate reference system with its origin at the first degree joint, the orthogonal system defined by the bone vector and vectors P and Q.
- the position of the second degree joint foot 420 a can be represented in spherical coordinates of a zenith angle ⁇ formed between shin bone 418 a and the bone vector 1102 and an azimuth angle, either ⁇ p or ⁇ q, formed by the perpendicular projection r′ of the shin bone r in the reference plane formed by P and Q which is orthogonal to the bone vector.
- FIG. 13 is a flowchart of an embodiment of a method for representing motion of a joint in the set of second degree joints attached to a joint in the set of first degree joints.
- software executing on a processor of a target recognition, tracking and analysis system translates 1202 the torso rigid body-based 3-D orthogonal coordinate reference system to a first degree joint to which an end of a bone attached to the respective second degree joint is attached.
- a first axis of a 3-D orthogonal body-based coordinate reference system with its origin at the first degree joint is generated 1204 which is a bone vector.
- the bone vector is created by extending a bone attached to a joint of the rigid body at one end and which is attached at the other end to the first degree joint also attached to the bone to which the respective second degree joint is attached.
- the software generates 1206 the second 3-D orthogonal coordinate system including the generated first axis, and defines 1208 the first axis as spherical north or the zenith and determines 1210 the position of the second degree joint using spherical coordinates with respect to the 3-D reference system centered at the first degree joint.
- the zenith angle theta ⁇ and the azimuth angle ⁇ referenced with respect to one of the axes, P or Q, defining an orthogonal reference plane with the zenith axis, the bone vector, are stored 1212 in a motion determination data set.
- a motion determination data set for each scaled skeletal data set is desired to be as small as possible and the least dimensions in space that need to be represented the better.
- the three angles will be part of the motion determination data set for each set to relate the moving body-based (a,b,c) system, which moves with the user's torso as the user moves, to the fixed camera-based reference system.
- the moving body-based (a,b,c) system which moves with the user's torso as the user moves
- the motion determination data set representing the change of positions of joints and bones in each frame is optimized to a lower entropy data set of 21 items for each frame.
- gestures may include body positions, angles or movements performed by a user which are recognized as a known, predefined movement such as a particular dance or exercise routine, or portion of a dance or exercise routine.
- a known, predefined movement such as a particular dance or exercise routine, or portion of a dance or exercise routine.
- the system determines whether a joint position and/or angle indicates that a known gesture was performed.
- the identification of a known gesture from the frames of image data processed as described above may be done by any of a variety of arbitrary classification systems. These systems receive, for example, 120 frames of dance (or other) motion represented by the model. This data sequence is supplied as input to a classifier which selects a predefined motion out of a number of predefined motions that most closely fits the data sequence. Classification is well known in machine learning, and numerous classifiers could be used. One of many such classifiers is explained below with reference to FIGS. 14 and 15 . Other classifiers may for example use Hidden Markov Models and logistic regression.
- FIG. 14 shows a block diagram of a gesture recognition engine 190 embodiment
- FIG. 15 shows a flowchart of an exemplar operation of the gesture recognition engine 190 of FIG. 14
- the gesture recognition engine 190 processes the motion determination data sets 500 .
- a gestures library 540 accessible by the gesture recognition engine 190 stores predefined gesture rules 542 to represent models of gestures against which a user's movements can be correlated. Other data formats besides rules can be used to define model gestures.
- each gesture may have a different, unique rule or set of rules 542 .
- a stored rule may define, for each parameter and for each body part, a single value, a range of values, a maximum value, a minimum value or an indication that a parameter for that body part is not relevant to the determination of the gesture covered by the rule. Rules may be created by a game author, by a host of the gaming platform or by users themselves.
- the parameters for the rules 542 are defined for data represented in the motion determination data sets.
- the parameters for the rules 542 are defined for data represented in the motion determination data sets.
- the 3 transformation angles relating the torso position to the fixed camera-based coordinate reference system and the spherical coordinate system angles of a zenith angle and an azimuth angle for each joint outside the rigid body.
- the gesture recognition engine 190 receives motion determination data sets for frames of a scaled skeletal data set, and generates 554 a set of candidate gestures.
- correlations are determined 554 between data items of the determination data sets with averages of model poses representing different gestures stored in the gestures library to generate the candidate gestures.
- a Fast Fourier Transform FFT
- the periodic nature of the signals allows the use of circular correlation and therefore FFT can be used for the computation.
- a probability value for each correlation is determined 556 that represents its correspondence to a candidate gesture over time. For example, a probability value for each correlation of a frame that it corresponds to a candidate gesture can be summed over the time period of the scaled skeletal data set to generate a sum of probability values. In one example, the sum can be weighted. For example, an exponentially decaying weighting can be used.
- a set of best match gestures are selected 558 which have the best correlation probability.
- the two gesture models having the highest sums of weighted correlation probabilities can be selected.
- the gesture recognition engine 190 selects 560 a match gesture from the set based on a criteria. For example, a logistic regression can be applied to the correlations with the two gesture models having the highest sums of probabilities.
- a linear function can be computed of its correlation probabilities, and the one with the larger linear function satisfies the criteria.
- step 516 in FIG. 5 indicates that a score and/or distance metric may be determined based on how well the user is performing a given recognized dance, exercise or other gesture.
- the score may be based on a distance metric, which is a determination of how closely the user's body positions, angles and/or movements match an “oracle” for the identified gesture.
- the oracle for an identified gesture represents the ideal body part positions, angles and/or movements set forth in the rule for the identified gesture.
- the oracle represents the data in the same format as the above-described computer model representation of the data.
- a score may be generated from this determination and the score may be displayed to the user on audiovisual device 16 .
- step 516 may further provide feedback to the user indicating that the user is performing a dance, exercise or other identified gesture well or poorly.
- This aspect may further indicate a particular body part of the user that is performing the identified gesture well or poorly.
- the highlighted leg on the displayed user avatar 21 indicates to the user that his kicks are not being performed properly and need improvement.
- some other distinctive visual aspect associated with a body part may indicate that the user's movements of that body part closely match the idealized movement for that body part in a given dance, exercise or other identified gesture.
- the indicator may be audible instead of or in addition to being visual.
- the positions, angles and/or movements for the oracle of a given stored gesture may be determined a variety of ways.
- a model of stored gesture information may have been captured of a professional instructor performing a gesture.
- the model may represent an average of other users who use the game or multimedia application, so the user can see how he compares to other users.
- a distance metric can be used to represent the user's deviation from the model. For example, a distance vector representing the user's movements derived from the motion determination data sets of the skeletal data can be compared with a vector of the movements of the oracle to determine the deviation. This deviation score can be summed across all joints to compute an overall score for the user's performance in the game or multimedia application. For example, the better the user performs dance moves, the better his score or advancement in the dance multimedia application.
Abstract
Description
- In the past, computing applications such as computer games and multimedia applications used controls to allow users to manipulate game characters or other aspects of an application. Typically such controls are input using, for example, controllers, remotes, keyboards, mice, or the like. More recently, computer games and multimedia applications have begun employing cameras and software gesture recognition engines to provide a human computer interface (“HCI”) or natural user interface (“NUI”). With HCI or NUI, user motions are detected, and some motions or poses represent gestures which are used to control game characters (e.g., a user's avatar) or other aspects of a multimedia application.
- In a natural user interface, an image capture device captures images of the user's motions in its field of view. The field of view can be represented as a finite Euclidean three-dimensional (3-D) space. A user can be performing a gesture with a body part but the user also tends to move around in the field of view of the capture device. A person extending her arm as a gesture while dancing about the room will generate a motion trajectory that will require more processing to decouple the arm action from the effect of the dancing motion. Additionally, users perform gestures at different speeds, providing another factor for which to account in gesture determination. Furthermore, the gesture recognition and subsequent responsive action need to be done in real-time and processed within the frame rate of the NUI system.
- Technology is presented for scalable, real-time motion recognition of a human body motion based on a skeletal model derived from image data of a user. The skeletal model represents the human body as a model of joints and bones and having a rigid body portion. Sets of skeletal data representing motion in terms of the human skeletal model are received which are defined in a camera-based three-dimensional (3-D) coordinate reference system. A camera-based 3-D reference system has a frame of reference defined with respect to an origin in a camera's field of view.
- Technology is further presented for temporal scaling the received skeletal data by synchronizing the sets of skeletal data to a predetermined number of sets for a number of periodic units. An example of a periodic unit is a repetitive beat of music. It is contemplated that the periodic unit may have a constant or variable frequency.
- In embodiments, the camera-based 3-D reference system may be spatially transformed to a body-based coordinate 3-D reference system having a frame of reference defined with respect to a position within the rigid body portion of the skeletal model. The body-based coordinate 3-D reference system is independent of the camera's field of view.
- For each set in the temporal scaled, spatial transformed skeletal data sets, the system determines motion of at least one body part using the rigid body-based 3-D reference system. Furthermore, gesture recognition is performed based on motion represented with respect to the body-based 3-D reference system.
- This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
- The systems, methods, and computer readable storage media for scalable real-time motion recognition based on skeletal data in accordance with this specification are further described with reference to the accompanying drawings.
-
FIG. 1A illustrates an example embodiment of a target recognition, analysis, and tracking system with which a user is interacting, and in which technology embodiments for scalable real-time motion recognition can operate. -
FIG. 1B illustrates embodiments of a camera field of view and a 3-D orthogonal coordinate reference system defined within the field of view. -
FIG. 1C illustrates an example of a visually distinctive indicator providing feedback to a user on his performance. -
FIG. 2 illustrates an example embodiment of a capture device that may be used in an embodiment of a target recognition, analysis, and tracking system. -
FIG. 3A illustrates an example embodiment of a computing environment that may be used to recognize human body motion in a target recognition, analysis, and tracking system. -
FIG. 3B illustrates another example embodiment of a computing environment that may be used to recognize human body motion in a target recognition, analysis, and tracking system. -
FIG. 4 illustrates an example of a skeletal model of a user that can be used by one or more embodiments. -
FIG. 5 is a flowchart of an embodiment of a method for scalable real-time gesture recognition. -
FIGS. 6A and 6B respectively illustrate an example of a captured set of frames and an example of a scaled set of frames. -
FIG. 7 illustrates an example of a skeletal model including a rigid body portion that can be used by one or more embodiments. -
FIG. 8 illustrates an example of a camera-based 3-D reference system and an example of a body-based 3-D reference system. -
FIG. 9 illustrates an example of a camera-based 3-D reference system superimposed with a body-based 3-D reference system with three angles for transforming between them. -
FIG. 10 illustrates an example of a skeletal model including a rigid body portion in which a body-based 3-D orthogonal coordinate system is translated from the origin within the rigid body to another joint within the rigid body. -
FIG. 11 is a flowchart of an embodiment of a method for representing motion of a joint in the set of first degree joints attached to the rigid body portion. -
FIG. 12A illustrates an example of another translation of a body-based 3-D coordinate system having an origin in the rigid body portion to at a first-degree joint in a set of the scaled skeletal data for determining motion of a second-degree joint. -
FIG. 12B illustrates an example of a 3-D orthogonal coordinate system centered at a first degree joint which is used for determining motion of a second degree joint attached to a common bone. -
FIG. 13 is a flowchart of an embodiment of a method for representing motion of a joint in the set of second degree joints. -
FIG. 14 shows a block diagram of a gesture recognition engine embodiment. -
FIG. 15 shows a flowchart of the operation of the gesture recognition engine ofFIG. 14 . - Referring initially to
FIG. 1A , the hardware for implementing the present technology includes a target recognition, analysis, andtracking system 10 which may be used to recognize, analyze, and/or track a human target such as theuser 18. Embodiments of the target recognition, analysis, andtracking system 10 include acomputing environment 12 for executing a gaming or other application, and anaudiovisual device 16 for providing audio and visual representations from the gaming or other application. Thesystem 10 further includes acapture device 20 for capturing positions and movements performed by the user, which the computing environment receives, interprets and uses to control the gaming or other application. Each of these components is explained in greater detail below. - As shown in
FIG. 1A , in an example embodiment, the application executing on thecomputing environment 12 may be a game with real time interaction such as a boxing game that theuser 18 may be playing. For example, thecomputing environment 12 may use theaudiovisual device 16 to provide a visual representation of aboxing opponent 19 to theuser 18. Thecomputing environment 12 may also use theaudiovisual device 16 to provide a visual representation of aplayer avatar 21 that theuser 18 may control with his or her movements. For example, theuser 18 may throw a punch in physical space to cause theplayer avatar 21 to throw a punch in game space. Thus, according to an example embodiment, thecomputer environment 12 and thecapture device 20 of the target recognition, analysis, and trackingsystem 10 may be used to recognize and analyze the punch of theuser 18 in physical space such that the punch may be interpreted as a game control of theplayer avatar 21 in game space. - Other movements by the
user 18 may also be interpreted as other controls or actions, such as controls to bob, weave, shuffle, block, jab, or throw a variety of different power punches.FIG. 1B illustrates embodiments of a camera field ofview 17 and a 3-D orthogonal coordinate reference system defined within the field of view. In this example,user 18 is interacting with a dance exercise application in which the user is dancing to music. The movements ofuser 18 are captured by thecapture device 20, which, in conjunction withcomputing environment 12, animates and controls the movements of an avatar on a display just like in the boxing example. Some of his movements may be gestures. - The origin of a 3-D orthogonal coordinate reference system is depicted in the center of the camera's 20 field of view which is located between the
user 18 and hisarm chair 23. A skeletal model as discussed below is derived from each captured image frame, and initially the skeletal model is represented in this camera-based coordinate system. This coordinate system is called camera-based because the position of the camera determines the field of view and the space is characterized using planes and normals defined with respect to the camera. The camera-based reference system is fixed. It does not move with the user. -
FIG. 1C illustrates an example of a visually distinctive indicator 11 displayed to a user on the user'savatar 21 onaudiovisual device 16 providing feedback to theuser 18 on his performance. His right leg ofavatar 21 is highlighted 11 on theaudiovisual device 16 to provide visual feedback that the user's leg lift with his right leg is not meeting a standard, for example height, of a leg lift gesture of a model or the average performed by others using the dance exercise game. - Suitable examples of a
system 10 and components thereof are found in the following co-pending patent applications, all of which are hereby specifically incorporated by reference: U.S. patent application Ser. No. 12/475,094, entitled “Environment and/or Target Segmentation,” filed May 29, 2009; U.S. patent application Ser. No. 12/511,850, entitled “Auto Generating a Visual Representation,” filed Jul. 29, 2009; U.S. patent application Ser. No. 12/474,655, entitled “Gesture Tool,” filed May 29, 2009; U.S. patent application Ser. No. 12/603,437, entitled “Pose Tracking Pipeline,” filed Oct. 21, 2009; U.S. patent application Ser. No. 12/475,308, entitled “Device for Identifying and Tracking Multiple Humans Over Time,” filed May 29, 2009, U.S. patent application Ser. No. 12/575,388, entitled “Human Tracking System,” filed Oct. 7, 2009; U.S. patent application Ser. No. 12/422,661, entitled “Gesture Recognizer System Architecture,” filed Apr. 13, 2009; U.S. patent application Ser. No. 12/391,150, entitled “Standard Gestures,” filed Feb. 23, 2009; and U.S. patent application Ser. No. 12/474,655, entitled “Gesture Tool,” filed May 29, 2009. -
FIG. 2 illustrates an example embodiment of thecapture device 20 that may be used in the target recognition, analysis, and trackingsystem 10. Further details relating to a capture device for use with the present technology are set forth in one or more of the above co-pending patent applications. However, in an example embodiment, thecapture device 20 may be configured to capture video having a depth image that may include depth values via any suitable technique including, for example, time-of-flight, structured light, stereo image, or the like. According to one embodiment, thecapture device 20 may organize the calculated depth information into “Z layers,” or layers that may be perpendicular to a Z axis extending from the depth camera along its line of sight. - As shown in
FIG. 2 , thecapture device 20 may include animage camera component 22. According to an example embodiment, theimage camera component 22 may be a depth camera that may capture the depth image of a scene. The depth image may include a two-dimensional (2-D) pixel area of the captured scene where each pixel in the 2-D pixel area may represent a length in, for example, centimeters, millimeters, or the like of an object in the captured scene from the camera. - As shown in
FIG. 2 , according to an example embodiment, theimage camera component 22 may include anIR light component 24, a three-dimensional (3-D)camera 26, and anRGB camera 28 that may be used to capture the depth image of a scene. For example, in time-of-flight analysis, theIR light component 24 of thecapture device 20 may emit an infrared light onto the scene and may then use sensors (not shown) to detect the backscattered light from the surface of one or more targets and objects in the scene using, for example, the 3-D camera 26 and/or theRGB camera 28. - According to another embodiment, the
capture device 20 may include two or more physically separated cameras that may view a scene from different angles, to obtain visual stereo data that may be resolved to generate depth information. - The
capture device 20 may further include amicrophone 30. Themicrophone 30 may include a transducer or sensor that may receive and convert sound into an electrical signal. According to one embodiment, themicrophone 30 may be used to reduce feedback between thecapture device 20 and thecomputing environment 12 in the target recognition, analysis, and trackingsystem 10. Additionally, themicrophone 30 may be used to receive audio signals that may also be provided by the user to control applications such as game applications, non-game applications, or the like that may be executed by thecomputing environment 12. - In an example embodiment, the
capture device 20 may further include aprocessor 32 that may be in operative communication with theimage camera component 22. Theprocessor 32 may include a standardized processor, a specialized processor, a microprocessor, or the like that may execute instructions for receiving the depth image, determining whether a suitable target may be included in the depth image, converting the suitable target into a skeletal representation or model of the target, or any other suitable instruction. - The
capture device 20 may further include amemory component 34 that may store the instructions that may be executed by theprocessor 32, images or frames of images captured by the 3-D camera or RGB camera, or any other suitable information, images, or the like. According to an example embodiment, thememory component 34 may include random access memory (RAM), read only memory (ROM), cache, Flash memory, a hard disk, or any other suitable storage component. As shown inFIG. 2 , in one embodiment, thememory component 34 may be a separate component in communication with theimage camera component 22 and theprocessor 32. According to another embodiment, thememory component 34 may be integrated into theprocessor 32 and/or theimage camera component 22. - As shown in
FIG. 2 , thecapture device 20 may be in communication with thecomputing environment 12 via acommunication link 36. Thecommunication link 36 may be a wired connection including, for example, a USB connection, a Firewire connection, an Ethernet cable connection, or the like and/or a wireless connection such as a wireless 802.11b, g, a, or n connection. According to one embodiment, thecomputing environment 12 may provide a clock to thecapture device 20 that may be used to determine when to capture, for example, a scene via thecommunication link 36. - Additionally, the
capture device 20 may provide the depth information and images captured by, for example, the 3-D camera 26 and/or theRGB camera 28, and a skeletal model that may be generated by thecapture device 20 to thecomputing environment 12 via thecommunication link 36. A variety of known techniques exist for determining whether a target or object detected bycapture device 20 corresponds to a human target. Skeletal mapping techniques may then be used to determine various spots on that user's skeleton, joints of the hands, wrists, elbows, knees, neck, ankles, shoulders, and where the pelvis meets the spine. Other techniques include transforming the image into a body model representation of the person and transforming the image into a mesh model representation of the person. - The skeletal model may then be provided to the
computing environment 12 such that the computing environment may track the skeletal model and render an avatar associated with the skeletal model. The computing environment may further determine which controls to perform in an application executing on the computer environment based on, for example, gestures of the user that have been recognized from the skeletal model. For example, as shown, inFIG. 2 , thecomputing environment 12 may include agesture recognition engine 190. Thegesture recognition engine 190 is explained hereinafter, but may in general include a collection of gesture filters, each comprising information concerning a gesture that may be performed by the skeletal model (as the user moves). The data captured by thecameras device 20 in the form of the skeletal model and movements associated with it may be compared to the gesture filters in thegesture recognition engine 190 to identify when a user (as represented by the skeletal model) has performed one or more gestures. Those gestures may be associated with various controls of an application. Thus, thecomputing environment 12 may use thegesture recognition engine 190 to interpret movements of the skeletal model and to control an application based on the movements. -
FIG. 3A illustrates an example embodiment of a computing environment that may be used to interpret one or more gestures in a target recognition, analysis, and tracking system. The computing environment such as thecomputing environment 12 described above with respect toFIGS. 1A-2 may be amultimedia console 100, such as a gaming console. As shown inFIG. 3A , themultimedia console 100 has a central processing unit (CPU) 101 having alevel 1cache 102, alevel 2cache 104, and aflash ROM 106. Thelevel 1cache 102 and alevel 2cache 104 temporarily store data and hence reduce the number of memory access cycles, thereby improving processing speed and throughput. TheCPU 101 may be provided having more than one core, and thus,additional level 1 andlevel 2caches flash ROM 106 may store executable code that is loaded during an initial phase of a boot process when themultimedia console 100 is powered ON. - A graphics processing unit (GPU) 108 and a video encoder/video codec (coder/decoder) 114 form a video processing pipeline for high speed and high resolution graphics processing. Data is carried from the
GPU 108 to the video encoder/video codec 114 via a bus. The video processing pipeline outputs data to an A/V (audio/video)port 140 for transmission to a television or other display. Amemory controller 110 is connected to theGPU 108 to facilitate processor access to various types ofmemory 112, such as, but not limited to, a RAM. - The
multimedia console 100 includes an I/O controller 120, asystem management controller 122, anaudio processing unit 123, anetwork interface controller 124, a first USB host controller 126, a second USB host controller 128 and a front panel I/O subassembly 130 that are preferably implemented on amodule 118. The USB controllers 126 and 128 serve as hosts for peripheral controllers 142(1)-142(2), awireless adapter 148, and an external memory device 146 (e.g., flash memory, external CD/DVD ROM drive, removable media, etc.). Thenetwork interface 124 and/orwireless adapter 148 provide access to a network (e.g., the Internet, home network, etc.) and may be any of a wide variety of various wired or wireless adapter components including an Ethernet card, a modem, a Bluetooth module, a cable modem, and the like. -
System memory 143 is provided to store application data that is loaded during the boot process. A media drive 144 is provided and may comprise a DVD/CD drive, hard drive, or other removable media drive, etc. The media drive 144 may be internal or external to themultimedia console 100. Application data may be accessed via the media drive 144 for execution, playback, etc. by themultimedia console 100. The media drive 144 is connected to the I/O controller 120 via a bus, such as a Serial ATA bus or other high speed connection (e.g., IEEE 1394). - The
system management controller 122 provides a variety of service functions related to assuring availability of themultimedia console 100. Theaudio processing unit 123 and anaudio codec 132 form a corresponding audio processing pipeline with high fidelity and stereo processing. Audio data is carried between theaudio processing unit 123 and theaudio codec 132 via a communication link. The audio processing pipeline outputs data to the A/V port 140 for reproduction by an external audio player or device having audio capabilities. - The front panel I/
O subassembly 130 supports the functionality of thepower button 150 and theeject button 152, as well as any LEDs (light emitting diodes) or other indicators exposed on the outer surface of themultimedia console 100. A systempower supply module 136 provides power to the components of themultimedia console 100. Afan 138 cools the circuitry within themultimedia console 100. - The
CPU 101,GPU 108,memory controller 110, and various other components within themultimedia console 100 are interconnected via one or more buses, including serial and parallel buses, a memory bus, a peripheral bus, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures can include a Peripheral Component Interconnects (PCI) bus, PCI-Express bus, etc. - When the
multimedia console 100 is powered ON, application data may be loaded from thesystem memory 143 intomemory 112 and/orcaches CPU 101. The application may present a graphical user interface that provides a consistent user experience when navigating to different media types available on themultimedia console 100. In operation, applications and/or other media contained within the media drive 144 may be launched or played from the media drive 144 to provide additional functionalities to themultimedia console 100. - The
multimedia console 100 may be operated as a standalone system by simply connecting the system to a television or other display. In this standalone mode, themultimedia console 100 allows one or more users to interact with the system, watch movies, or listen to music. However, with the integration of broadband connectivity made available through thenetwork interface 124 or thewireless adapter 148, themultimedia console 100 may further be operated as a participant in a larger network community. - When the
multimedia console 100 is powered ON, a set amount of hardware resources are reserved for system use by the multimedia console operating system. These resources may include a reservation of memory (e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth (e.g., 8 kbs), etc. Because these resources are reserved at system boot time, the reserved resources do not exist from the application's view. - In particular, the memory reservation preferably is large enough to contain the launch kernel, concurrent system applications and drivers. The CPU reservation is preferably constant such that if the reserved CPU usage is not used by the system applications, an idle thread will consume any unused cycles.
- With regard to the GPU reservation, lightweight messages generated by the system applications (e.g., popups) are displayed by using a GPU interrupt to schedule code to render popup into an overlay. The amount of memory required for an overlay depends on the overlay area size and the overlay preferably scales with screen resolution. Where a full user interface is used by the concurrent system application, it is preferable to use a resolution independent of the application resolution. A scaler may be used to set this resolution such that the need to change frequency and cause a TV resynch is eliminated.
- After the
multimedia console 100 boots and system resources are reserved, concurrent system applications execute to provide system functionalities. The system functionalities are encapsulated in a set of system applications that execute within the reserved system resources described above. The operating system kernel identifies threads that are system application threads versus gaming application threads. The system applications are preferably scheduled to run on theCPU 101 at predetermined times and intervals in order to provide a consistent system resource view to the application. The scheduling is to minimize cache disruption for the gaming application running on the console. - When a concurrent system application requires audio, audio processing is scheduled asynchronously to the gaming application due to time sensitivity. A multimedia console application manager (described below) controls the gaming application audio level (e.g., mute, attenuate) when system applications are active.
- Input devices (e.g., controllers 142(1) and 142(2)) are shared by gaming applications and system applications. The input devices are not reserved resources, but are to be switched between system applications and the gaming application such that each will have a focus of the device. The application manager preferably controls the switching of input stream, without knowledge of the gaming application's knowledge and a driver maintains state information regarding focus switches. The
cameras capture device 20 may define additional input devices for theconsole 100. -
FIG. 3B illustrates another example embodiment of acomputing environment 220 that may be the computingenvironment 12 shown inFIGS. 1A-2 used to interpret one or more gestures in a target recognition, analysis, and tracking system. Thecomputing system environment 220 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the presently disclosed subject matter. Neither should thecomputing environment 220 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in theexemplary operating environment 220. In some embodiments, the various depicted computing elements may include circuitry configured to instantiate specific aspects of the present disclosure. For example, the term circuitry used in the disclosure can include specialized hardware components configured to perform function(s) by firmware or switches. In other example embodiments, the term circuitry can include a general purpose processing unit, memory, etc., configured by software instructions that embody logic operable to perform function(s). In example embodiments where circuitry includes a combination of hardware and software, an implementer may write source code embodying logic and the source code can be compiled into machine readable code that can be processed by the general purpose processing unit. Since one skilled in the art can appreciate that the state of the art has evolved to a point where there is little difference between hardware, software, or a combination of hardware/software, the selection of hardware versus software to effectuate specific functions is a design choice left to an implementer. More specifically, one of skill in the art can appreciate that a software process can be transformed into an equivalent hardware structure, and a hardware structure can itself be transformed into an equivalent software process. Thus, the selection of a hardware implementation versus a software implementation is one of design choice and left to the implementer. - In
FIG. 3B , thecomputing environment 220 comprises acomputer 241, which typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed bycomputer 241 and includes both volatile and nonvolatile media, removable and non-removable media. The system memory 222 includes computer storage media in the form of volatile and/or nonvolatile memory such asROM 223 andRAM 260. A basic input/output system 224 (BIOS), containing the basic routines that help to transfer information between elements withincomputer 241, such as during start-up, is typically stored inROM 223.RAM 260 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 259. By way of example, and not limitation,FIG. 3B illustrates operating system 225, application programs 226,other program modules 227, and program data 228. - The
computer 241 may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,FIG. 3B illustrates a hard disk drive 238 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 239 that reads from or writes to a removable, nonvolatile magnetic disk 254, and an optical disk drive 240 that reads from or writes to a removable, nonvolatile optical disk 253 such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive 238 is typically connected to the system bus 221 through a non-removable memory interface such as interface 234, and magnetic disk drive 239 and optical disk drive 240 are typically connected to the system bus 221 by a removable memory interface, such as interface 235. - The drives and their associated computer storage media discussed above and illustrated in
FIG. 3B , provide storage of computer readable instructions, data structures, program modules and other data for thecomputer 241. InFIG. 3B , for example, hard disk drive 238 is illustrated as storing operating system 258, application programs 257, other program modules 256, and program data 255. Note that these components can either be the same as or different from operating system 225, application programs 226,other program modules 227, and program data 228. Operating system 258, application programs 257, other program modules 256, and program data 255 are given different numbers here to illustrate that, at a minimum, they are different copies. A user may enter commands and information into thecomputer 241 through input devices such as akeyboard 251 and apointing device 252, commonly referred to as a mouse, trackball or touch pad. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 259 through a user input interface 236 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). Thecameras capture device 20 may define additional input devices for theconsole 100. Amonitor 242 or other type of display device is also connected to the system bus 221 via an interface, such as avideo interface 232. In addition to the monitor, computers may also include other peripheral output devices such asspeakers 244 and printer 243, which may be connected through an output peripheral interface 233. - The
computer 241 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 246. The remote computer 246 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to thecomputer 241, although only a memory storage device 247 has been illustrated inFIG. 3B . The logical connections depicted inFIG. 3B include a local area network (LAN) 245 and a wide area network (WAN) 249, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. - When used in a LAN networking environment, the
computer 241 is connected to the LAN 245 through a network interface or adapter 237. When used in a WAN networking environment, thecomputer 241 typically includes a modem 250 or other means for establishing communications over the WAN 249, such as the Internet. The modem 250, which may be internal or external, may be connected to the system bus 221 via the user input interface 236, or other appropriate mechanism. In a networked environment, program modules depicted relative to thecomputer 241, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,FIG. 3B illustrates remote application programs 248 as residing on memory device 247. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. -
FIG. 4A depicts an example skeletal mapping of a user that may be generated from thecapture device 20. In this embodiment, a variety of joints and bones are identified: each hand 402, eachforearm 404, each elbow 406, each bicep 408, each shoulder 410, each hip 412, each thigh 414, each knee 416, each foreleg 418, each foot 420, thehead 422, themid spine 424, the top 426 and thebottom 428 of the spine, and thewaist 430. Where more points are tracked, additional features may be identified, such as the bones and joints of the fingers or toes, or individual features of the face, such as the nose and eyes. It is understood that one or more of the points shown inFIG. 4A may be omitted and/or others may be added. Moreover, a skeletal mapping is one example of a computer model of a user, and other computer models are contemplated. - Each of the points in
FIG. 4A may be described in 3-dimensional Cartesian space by an x, y and z coordinate in a frame of reference with respect to the capture device 20 (camera space). One consequence of this is that any absolute motion of a joint in this reference frame needs to be computed. For example, referring again toFIG. 1B if theuser 18 is moving in the frame of reference with his hand 18 a straight down and stationary with respect to his body, that hand is nonetheless moving in camera space frame of reference. Time and processor resources are required to determine its movement in camera space relative to a prior frame. - It is an aspect of the present technology to conform, or transform, the frame of reference of body parts from camera space where absolute motion is measured to a frame of reference where motion is measured relative to an “upstream” joint. This frame of reference is referred to as a body space or body frame of reference. In one embodiment, an upstream joint is the next adjacent joint closer to the torso. So the upstream joint of the wrist is the elbow, and the upstream joint of the elbow is the shoulder; the upstream joint of the ankle is the knee, and the upstream joint of the knee is the hip.
- Rigid-body transformation (e.g., translation and rotation) from the camera frame of reference to the body frame of reference provides the same information as to joint position, but does so in more efficient and low entropy manner. Continuing with the above example where the user is moving through the field of view with his hand 18 a stationary at his side, while moving in absolute (camera) space, the user's hand is not moving relative to its upstream joint. Thus, tracking the user's hand in body space simplifies joint tracking from frame to frame. In general, tracking movement of joints relative to other joints results in smaller search space and data set, and faster processing and gesture recognition as explained below. It is a representation which is invariant to the group of similarity transformations (scaling, rotation, translation) in 3D.
- As is also explained below, another aspect of the present technology is to treat the torso, including the shoulders and hips, as a rigid body. This good approximation allows the torso to be described with three angles, described below, relative to camera space, simplifying skeletal tracking
- An embodiment of the present technology will now be explained with reference to the flowchart of
FIG. 5 . The method may be performed by software executing on one or more processors such as theprocessor 32 ofcapture device 20, the CPU 101 (FIG. 3A ) or the processing unit 299 (FIG. 3B ) of thecomputing environment 12 for a target recognition, analysis tracking andtracking system 10. - Frames of skeletal model data are received in
step 502 fromcapture device 20 representing a human body in a three-dimensional space with respect to the fixed camera-based 3-D coordinate reference system. Optionally, the size of bones in the sets of skeletal data are normalized 504. In particular, different users may be of different sizes, with limbs and torsos of different sizes. Instep 504, the distances measured between joints for a given user are normalized to a standard length for such distances. This can be done on the received skeletal model data or on the scaled skeletal data sets. In embodiments, the normalization of the size of bones instep 506 may be omitted. - One goal of the present system is to identify movements, such as dance movements of a user when dancing to music played by an application running on computing
environment 12. The present system makes use of the fact that movements, such as dance movements, are typically repetitive. There are basic movements at each beat of the music, with a combination of these basic movements forming a multi-beat motion that itself repeats. Thus, a user may repeat a given movement once per beat of music, or sets of beats. As the music speeds up, the user tends to move faster. As the music slows down, the user tends to move slower. The result is that the movements a user tends to make repeat every beat, or predefined number of beats. Accordingly, the present system analyzes repetitive movements over a period not based in time, but rather based on the beat of the music (or other periodic unit of measurement). - In particular, in
step 506, software executing in the system normalizes the number of frames of skeletal data to a periodic unit of measurement to provide normalized skeletal data sets. An example of a periodic unit of measure is a predefined number of beats in music. By normalizing the number of frames to the beat of music, or some other periodic unit of measurement, the present system is able to normalize repetitive user movements to a fixed period, independent of time. For music having a faster beat, the number of frames in the period over which a user completes one cycle of movement will be faster. For music having a slower beat, the number of frames in the period over which a user completes a cycle of movement will be slower. However, the period itself is independent of time. - The beat of the music and how it changes in a piece is predetermined generally, but it can be detected as well using music software if necessary. By using beats as a reference, rather than time, gestures can be recognized independently of the speed at which they are made. Normalizing the number of frames to a beat or other period simplifies calculations in real-time gesture recognition by making it easier to identify repetitive movements within repetitive fixed periods. This information may for example be used to identify gestures or specific movements, such as dance movements, as explained below with respect to
FIGS. 14 and 15 . -
FIGS. 6A and 6B illustrate an example of a captured set of skeletal data frames derived from received frames of images captured by thecapture device 20, referred to as skeletal data frames, and a scaled set of skeletal data frames respectively. In this illustrative example, the periodic unit is a beat, and the number of repeating units is 8 beats for a repeating period, called a beat period in this example. - For a selected number of periodic units in a repeating period, a number of frames is determined. For example, the number of frames can be the number of frames captured in a repeating period based on the frame rate of the
capture device 20. In this example, the beat period is 8 beats, and the normalized number of frames for the beat period is 120. At a frame rate for theimage capture device 20 of 30 frames per second, and a beat rate of 0.5 seconds, there are 8 beats every 4 seconds and 120 frames in 4 seconds. Based on knowledge of system parameters in thesystem 10, the start of the repeating period is synchronized with the start of the frame capture. - However, some frames are dropped like skipped
frames FIG. 6A . Theimage capture device 20 did not capture an image or occlusion of the user occurred so that a skeleton was not generated for that frame. Skeleton data for the other frames, 602, 606-612 and 616-618 was captured. - In order to have 120 frames of data with skeletons, a frame is interpolated for each dropped frame. In one example, data for a frame can be interpolated from the frames which came before and after the dropped frame. A frame typically has a time stamp which indicates its place in a succession of frames. For example, interpolated frame 604 i is generated based on the skeleton data of
frames frames frame 602 to another inframe 606. Interpolated frame 604 i may represent a halfway point for the elbow joint between the two positions. In this example, missing frames are interpolated to produce the exemplar set of scaled skeletal data. - In general, the present system captures information relating to the position of a skeleton, over the predefined repeating period discussed above, and determines whether the user is performing a known movement over that period. In order to reduce the data required for this analysis, the frame of reference is transformed to a body-based frame of references, where movement of body parts relative to each other, and not the field of view as a whole, are considered. Once the body-based frame of reference is defined, positions of joints relative to each other are measured for each frame. This position and angle information may then be compared against stored information to identify whether the user is performing some known movement. Each of these operations is explained below.
- In
step 508, software executing in thecapture device 20 or thecomputing environment 12 or both generates a body-based coordinate 3-D reference system having a frame of reference defined with respect to a position within a rigid body portion of a skeletal model. As indicated above and as shown inFIG. 7 , the present technology models a group of joints as a singlerigid body 432. In one embodiment, the joints in therigid body 432 include the neck joint 426, the twoshoulder joints mid-spine 424 andlower spine 428 joints, and thehip joints rigid body portion 432 may include fewer joints, or additional joints, in further embodiments. - The joints in the rigid body have little or no relative motion with respect to each other and, as such, are treated as a single rigid body by the present technology. Joints within the torso in reality do move, but the motion is to a much lesser extent than for a knee or a hand. The
rigid body portion 432 shown in the model ofFIG. 7 can be treated as a single block representing the user's body position in the camera-based 3-D coordinate system for the camera's field ofview 17. If theuser 18 moves left or right, forward or backward, his torso does as well and can be treated as a single unit, or rigid body. Torso data is robust in that data detected for the torso has a high probability of accuracy even when the data is very noisy. - Because the torso can be treated as a rigid body, principal component analysis (PCA) can be used to define an orthogonal 3-D coordinate system having the user's body as the frame of reference. PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate axis (called the first principal component), the second greatest variance on the second coordinate, and the third axis is the cross-product of the first two axes.
-
FIG. 8 illustrates an example of a fixed camera-based 3-D reference system 5 and an example of a body-based 3-D reference system 800. In this example, the a-axis is the axis of maximum variability, the length of the spine inFIG. 4B , the b-axis the axis of next most variability, the axis from shoulder to shoulder, and the c-axis the cross product of axes a and b, a ray out from the chest of a user. The orientation of the a, b,c system 800 varies within the camera-basedreference system 5. - In
step 508, thesystem 10 performs PCA on data points, the shoulders, neck, spine joints, and hip joints (seven joints), of therigid body portion 432 of theskeletal model 400 to obtain axes of an orthogonal coordinate system defined with respect to the rigid body portion. For each frame, thecapture device 20 or thecomputing environment 12 or both together return a skeletal data set representing the position of the user in the body-based reference frame. -
FIGS. 7 and 8 show examples of a body-based 3-D coordinatereference system embodiment 800 having an origin (0,0,0) at the neck 426 (labeled inFIG. 7 ), with an a-axis extending along the spine, a b-axis extending across the shoulders and orthogonal to the a-axis, and a c-axis orthogonal to the plane formed by the a and b axes and representing motion in and out of that plane. The positive c-axis extends from the neck out of the page. It is understood that the origin may be positioned at locations within therigid body 432 other than the neck joint 426. - The vectors a, b and c determined by PCA will be based on the user position relative to the camera. Computing the vectors a, b and c over the torso by PCA provides an orientation of the torso with respect to Cartesian space. Depending on where the
capture device 20 is, the vectors a, b and/or c will have a different orientation. - In embodiments, the next step (510) in the process involves describing the rigid body-based vectors a, b and c in terms of the camera-based 3-D coordinate system for each frame of the scaled skeletal data. While this may be done by a variety of methods, in one example explained below, Euler angles may be used for this transformation. In one example, the type of Euler angles used are pitch, yaw and roll, also known as Tait-Bryan angles. Other types of Euler angles may be used in further embodiments. Another possible method for this transformation beside Euler angles is a direction of cosines approach.
-
FIG. 9 illustrates the coordinatesystems FIG. 9 , the intersection of the xy and the be coordinate planes is vector (N) which is a line perpendicular to both the z and a-axis, sometimes referred to as the line of nodes. Alpha a is the angle between the x-axis and the vector N. Beta β is the angle between the z-axis and the a-axis. Gamma γ is the angle between the vector N and the c-axis. In other examples, the angles could be between other axes pairings just as long as the transformation angles represent consistent pairings. - A composition of rotations is composing three rotations, each around a single axis. In this way, Euler angles can be used to represent the relative orientation of the body based coordinate system to the camera-based coordinate system. For each rotation, Euler rotations are defined as the movement obtained by changing one of the Euler angles while leaving the other two constant. Euler rotations are expressed in terms of the camera-based coordinate system, the body based coordinate system and intermediate coordinate reference systems. There are also a number of valid known sequences of rotations. The axis selected for the start of rotation is arbitrary, but it cannot be used twice in succession. For example, using these mixed axes of rotation, the first angle moves the line of nodes around the fixed axis z, the second rotates around the vector N, and the third one is an intrinsic rotation around an axis fixed in the body that moves, in this example axis c.
- Additional information on transformation between coordinate systems using Euler angles is provided at http://en.wikipedia.org/wiki/Euler_angles and http://www.aoe.vt.edu/˜durham/AOE5214/Ch03.pdf, Ch. 3 Coordinate System Transformations, pp. 23-39, which sources of this information are incorporated by reference herein in their entirety. It is understood that transformations between camera-based 3-D space and body-based 3-D space may be made by a variety of other known transformation matrices and equations. For example, the orientation of a rigid body can be represented by an orientation matrix, which includes, in its three columns, the Cartesian coordinates of three points. These points are used to define the orientation of the axes of the local system; they are the tips of three unit vectors aligned with those axes.
- With each set of capture data, e.g., each frame of the scaled skeletal data, the three Euler angles are transformation angles which can be used to represent the position of the rigid body torso in terms of the camera-based orthogonal 3-D coordinate
system 5 in order to determine motion of the torso. For example, this determination can tell if a user is standing sideways or is twisting his torso. The software executing on a processor in the target recognition, analysis, and trackingsystem 10 determines a Euler transformation angle α, β, γ between each axis of the rigid body coordinate system and an axis of the fixed camera-based coordinate system, and these three angles form part of a motion determination data set for each scaled data set. - With the definition of the torso in terms of the camera-based orthogonal 3-D coordinate
system 5, for example by Euler angles as described above, the torso is now defined independently of the camera position. The next step (512) is to describe the positions of first degree and second degree joints extending from the torso. In particular, if the location of a joint is known with respect to the torso, and the torso's location is known with respect to the fixed camera-based 3-D coordinate system, the absolute motion of the joint, for example an elbow, can be represented in terms of the relative motion of the joint with respect to the torso, and the motion of torso with respect to the camera-based 3-D reference system. - Referring again to
FIG. 7 , the first degree joints are those which are one joint away from the rigid torso (elbow joints 406 and knee joints 416), as indicated by circles inFIG. 7 . These first degree joints may be thought of as being anchored to the rigid body by a constant bone length. The second degree joints are those which are one joint away from the first degree joints (wrist joints 402 and ankle joints 420), as indicated by squares inFIG. 7 . These second degree joints may be thought of as being anchored to the first degree joints by a constant bone length. In one example, the skeletal model ofFIG. 7 can be thought of as a tree structure in which the rigid body torso is the base of the tree, and the user's arms, legs and head forming branches extending from the base of the tree. -
FIG. 10 illustrates an example in which a body-based 3-D orthogonal coordinate system of a skeletal model including a rigid body portion is translated from the origin within the rigid body to another joint within the rigid body. For a joint in the set of first degree joints, the joint in the rigid body to which it is attached is stationary and can be treated as an origin of a coordinate reference system. For example, inFIG. 10 , the origin is translated from the neck joint 426 along the b-axis to theright shoulder 410 b so that the position of theleft elbow 406 b may be calculated relative to the left shoulder. Given the normalized, known translation distances between joints, the distance from neck to right shoulder is known. - Additionally, within the rigid body, the joints are deemed stationary and separated by known, normalized distances between joints. Thus, for example, a translation of the reference system having its origin at the neck can be linearly translated to another joint in the rigid body. For example, as shown in
FIG. 9 , the origin at neck joint 426 is translated along the b-axis to theright shoulder 410 b. - The present system treats the same body parts in human bodies as connected in a known, tree-like relation. For example, the
right elbow 406 b is connected to theright shoulder 410 b via anupper arm bone 408 b. This known connectedness in addition to the bone sizes being normalized allows the system to predetermine the translation of the origin of the a, b, c vector reference system for the different joints of the skeleton. -
FIG. 11 is a flowchart providing further detail ofstep 512 for representing motion of a joint in the set of first degree joints attached to the rigid body portion. In this embodiment, software executing on a processor of a target recognition, tracking and analysis system translates 1002 the frame of reference for the 3-D orthogonal body-based coordinate system from an origin of the system on the rigid body to a joint of the rigid body to which an end of a bone attached to the respective first degree joint is attached. The position of the first degree joint is determined 1004 using spherical coordinates with respect to the body-based 3-D reference system. A point in 3-D space can be represented by a radial distance r from a fixed origin, an inclination or zenith angle θ measured from a fixed zenith direction, and an azimuth angle φ of its orthogonal projection on a reference plane that passes through the origin and is orthogonal to the zenith. The azimuth angle is measured from a fixed reference axis on the plane. (See http://en.wikipedia.org/wiki/Spherical_coordinate_system.) - Referring back to
FIG. 10 for illustrative purposes only and not to be limiting thereof, theupper arm bone 408 b between theright shoulder 410 b andright elbow 406 b is the radial distance. This is a constant, so the system need not calculate or store its length for each set of the scaled data. The a-axis acts as the zenith or spherical north which forms the zenith angle or inclination angle theta θ. This angle theta θ is measured between the a-axis and theupper arm bone 408 b. Axes b and c form a reference plane passing through the origin with a and orthogonal to a as zenith in which the perpendicular projection r′ of theupper arm 408 b is projected. The azimuth angle φb is measured from the b-axis to the projection r′ and the azimuth angle φc is measured from the c-axis to the projection r′. Due to being in the same plane and being measured from fixed reference axes, φ with respect to one planar axis can be determined from φ with respect to the other axis. Mathematically, φc=π/2−φb. However, if −π/2<φb,c<π/2, then the information from φb and φc is not redundant. Thus, θ may be stored, and, in different embodiments, one or both angles φb and φc may be stored. - The zenith angle theta θ and the azimuth angle φ referenced with respect to one of the axes, b or c, defining the orthogonal reference plane in which the perpendicular projection of the bone is made, φb or φc, are stored 1006 in a motion determination data set for the first degree joint in one of the sets of scaled skeletal data
-
FIG. 12A illustrates an example of another transformation of a body-based 3-D coordinate system having an origin in the rigid body portion to at a first degree joint in a set of the scaled skeletal data for determining motion of a second degree joint. The foot joint 420 a is in the set of second degree joints in which each respective joint is attached to abone 418 a, another end of which is attached to one of the joints in the set of first degree joints, in thiscase knee 416 a. Just as the torso based coordinate system was moved within the rigid body, the origin can be translated into the first degree knee joint 416 a which is attached via abone 414 a to the hip joint 412 a within therigid body torso 432. Theknee 416 a moves around, but the rigid body torso based 3-D coordinate reference system provides robust or accurate data with respect to the axis c coming out of the chest of the skeletal model. Additionally, thebone 414 a between thehip 412 a and theknee 416 a are very reliable and thefoot 420 a is always attached to theknee 416 a. It is also constant size due to the normalization. Thebone 414 a between the hip and knee is extended out the other side of the knee and acts as a zenith or spherical north from which a zenith or inclination angle theta θ can be defined with theshin bone 418 a between theknee 416 a and thefoot 420 a. - However, the
bone extension vector 1102 is not necessarily orthogonal to the c vector of the torso transformed coordinatesystem 800 of axes a,b and c. -
FIG. 12B illustrates an example of a 3-D orthogonal coordinate system centered at a first degree joint which is used for determining motion of a second degree joint attached to a common bone. An orthogonal 3-D coordinate system is generated with theknee 416 a as origin and thebone extension 1102 as the zenith axis. The cross product of the c vector and thebone vector 1102 results in vector P, and another orthogonal vector Q is generated by crossing thebone vector 1102 with P. -
FIG. 12B represents the relationship between the second degree joint, foot 420 a, and a first degree joint,knee 416 a, using another 3-D coordinate reference system with its origin at the first degree joint, the orthogonal system defined by the bone vector and vectors P and Q. Now the position of the second degreejoint foot 420 a can be represented in spherical coordinates of a zenith angle θ formed betweenshin bone 418 a and thebone vector 1102 and an azimuth angle, either φp or φq, formed by the perpendicular projection r′ of the shin bone r in the reference plane formed by P and Q which is orthogonal to the bone vector. -
FIG. 13 is a flowchart of an embodiment of a method for representing motion of a joint in the set of second degree joints attached to a joint in the set of first degree joints. In this embodiment, software executing on a processor of a target recognition, tracking and analysis system, translates 1202 the torso rigid body-based 3-D orthogonal coordinate reference system to a first degree joint to which an end of a bone attached to the respective second degree joint is attached. - A first axis of a 3-D orthogonal body-based coordinate reference system with its origin at the first degree joint is generated 1204 which is a bone vector. The bone vector is created by extending a bone attached to a joint of the rigid body at one end and which is attached at the other end to the first degree joint also attached to the bone to which the respective second degree joint is attached.
- The software generates 1206 the second 3-D orthogonal coordinate system including the generated first axis, and defines 1208 the first axis as spherical north or the zenith and determines 1210 the position of the second degree joint using spherical coordinates with respect to the 3-D reference system centered at the first degree joint. The zenith angle theta θ and the azimuth angle φ referenced with respect to one of the axes, P or Q, defining an orthogonal reference plane with the zenith axis, the bone vector, are stored 1212 in a motion determination data set. A motion determination data set for each scaled skeletal data set is desired to be as small as possible and the least dimensions in space that need to be represented the better.
- As mentioned above, the three angles will be part of the motion determination data set for each set to relate the moving body-based (a,b,c) system, which moves with the user's torso as the user moves, to the fixed camera-based reference system. In the example model of
FIG. 7 , there are 9 joints outside the rigid body. In the examples discussed above, because the bone sizes were normalized, only the spherical coordinate systems zenith and azimuth angles for each joint is stored in the motion determination data set. For these examples, the motion determination data set representing the change of positions of joints and bones in each frame is optimized to a lower entropy data set of 21 items for each frame. - Once the positions and angles of different joints in the body are identified, this information may be compared against stored information for known gestures. As used herein, gestures may include body positions, angles or movements performed by a user which are recognized as a known, predefined movement such as a particular dance or exercise routine, or portion of a dance or exercise routine. As indicated at
step 514 inFIG. 5 , once joint positions and angles have been identified, the system determines whether a joint position and/or angle indicates that a known gesture was performed. - The identification of a known gesture from the frames of image data processed as described above may be done by any of a variety of arbitrary classification systems. These systems receive, for example, 120 frames of dance (or other) motion represented by the model. This data sequence is supplied as input to a classifier which selects a predefined motion out of a number of predefined motions that most closely fits the data sequence. Classification is well known in machine learning, and numerous classifiers could be used. One of many such classifiers is explained below with reference to
FIGS. 14 and 15 . Other classifiers may for example use Hidden Markov Models and logistic regression. -
FIG. 14 shows a block diagram of agesture recognition engine 190 embodiment, andFIG. 15 shows a flowchart of an exemplar operation of thegesture recognition engine 190 ofFIG. 14 . Thegesture recognition engine 190 processes the motion determination data sets 500. In this embodiment, agestures library 540 accessible by thegesture recognition engine 190 stores predefined gesture rules 542 to represent models of gestures against which a user's movements can be correlated. Other data formats besides rules can be used to define model gestures. In embodiments, each gesture may have a different, unique rule or set ofrules 542. A stored rule may define, for each parameter and for each body part, a single value, a range of values, a maximum value, a minimum value or an indication that a parameter for that body part is not relevant to the determination of the gesture covered by the rule. Rules may be created by a game author, by a host of the gaming platform or by users themselves. - In this embodiment, the parameters for the
rules 542 are defined for data represented in the motion determination data sets. In other words, in terms of the 3 transformation angles relating the torso position to the fixed camera-based coordinate reference system, and the spherical coordinate system angles of a zenith angle and an azimuth angle for each joint outside the rigid body. - In
step 550, thegesture recognition engine 190 receives motion determination data sets for frames of a scaled skeletal data set, and generates 554 a set of candidate gestures. In this example, correlations are determined 554 between data items of the determination data sets with averages of model poses representing different gestures stored in the gestures library to generate the candidate gestures. Due to the quasi-periodic nature of the pose information represented in the parameter information for the rules and the motion determination data sets, a Fast Fourier Transform (FFT) can be applied to the motion determination data sets for faster determination of correlation of the user's movements with gesture models. The periodic nature of the signals allows the use of circular correlation and therefore FFT can be used for the computation. - A probability value for each correlation is determined 556 that represents its correspondence to a candidate gesture over time. For example, a probability value for each correlation of a frame that it corresponds to a candidate gesture can be summed over the time period of the scaled skeletal data set to generate a sum of probability values. In one example, the sum can be weighted. For example, an exponentially decaying weighting can be used.
- A set of best match gestures are selected 558 which have the best correlation probability. For example, the two gesture models having the highest sums of weighted correlation probabilities can be selected. The
gesture recognition engine 190 selects 560 a match gesture from the set based on a criteria. For example, a logistic regression can be applied to the correlations with the two gesture models having the highest sums of probabilities. For each of the two best candidate gestures, a linear function can be computed of its correlation probabilities, and the one with the larger linear function satisfies the criteria. - Once a predefined gesture is recognized, this information may be used in a variety of ways. For example,
step 516 inFIG. 5 indicates that a score and/or distance metric may be determined based on how well the user is performing a given recognized dance, exercise or other gesture. In examples, the score may be based on a distance metric, which is a determination of how closely the user's body positions, angles and/or movements match an “oracle” for the identified gesture. The oracle for an identified gesture represents the ideal body part positions, angles and/or movements set forth in the rule for the identified gesture. The oracle represents the data in the same format as the above-described computer model representation of the data. A score may be generated from this determination and the score may be displayed to the user onaudiovisual device 16. - In one embodiment, step 516 may further provide feedback to the user indicating that the user is performing a dance, exercise or other identified gesture well or poorly. This aspect may further indicate a particular body part of the user that is performing the identified gesture well or poorly. For example, in
FIG. 1C , the highlighted leg on the displayeduser avatar 21 indicates to the user that his kicks are not being performed properly and need improvement. Alternatively, some other distinctive visual aspect associated with a body part may indicate that the user's movements of that body part closely match the idealized movement for that body part in a given dance, exercise or other identified gesture. The indicator may be audible instead of or in addition to being visual. - The positions, angles and/or movements for the oracle of a given stored gesture may be determined a variety of ways. For example, a model of stored gesture information may have been captured of a professional instructor performing a gesture. In another example, the model may represent an average of other users who use the game or multimedia application, so the user can see how he compares to other users.
- In one example, a distance metric can be used to represent the user's deviation from the model. For example, a distance vector representing the user's movements derived from the motion determination data sets of the skeletal data can be compared with a vector of the movements of the oracle to determine the deviation. This deviation score can be summed across all joints to compute an overall score for the user's performance in the game or multimedia application. For example, the better the user performs dance moves, the better his score or advancement in the dance multimedia application.
- Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
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