CN111666782A - Method and apparatus for motion state detection - Google Patents
Method and apparatus for motion state detection Download PDFInfo
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Abstract
The embodiment of the application discloses a method and a device for detecting a motion state, electronic equipment and a computer readable medium. One embodiment of the method comprises: acquiring a target image comprising a human face image; inputting the target image into a pre-trained motion state detection model to obtain a motion state detection result of the target image, wherein the motion state detection model is used for representing a corresponding relation between an image comprising a human face image and the motion state detection result of the image; and in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state, determining user information corresponding to the human face image. This embodiment enriches the flexibility of motion state detection.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to the technical field of internet, and particularly relates to a method and a device for detecting a motion state.
Background
The related motion state detection method generally detects the heart rate through wearable equipment, reminds the athlete through the way of screen display beside the track and the like when the heartbeat is too fast, so that the athlete can timely know the physical condition, and further life threatening conditions are avoided. However, as the number of people increases, the hardware cost for using wearable devices is high, and the applicable scenarios are limited.
Disclosure of Invention
The embodiment of the application provides a method and a device for detecting a motion state.
In a first aspect, an embodiment of the present application provides a method for motion state detection, where the method includes: acquiring a target image comprising a human face image; inputting the target image into a pre-trained motion state detection model to obtain a motion state detection result of the target image, wherein the motion state detection model is used for representing a corresponding relation between an image comprising a human face image and the motion state detection result of the image; and determining user information corresponding to the human face image in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state.
In some embodiments, the method further comprises: and sending prompt information representing the excessive movement state to the target client, wherein the prompt information comprises the determined user information.
In some embodiments, the motion state detection model is trained by: acquiring a sample set, wherein the sample comprises a sample image of a human face image and a sample motion state detection result corresponding to the sample image; performing the following training steps based on the sample set: respectively inputting a sample image of at least one sample in a sample set to an initial neural network to obtain a motion state detection result corresponding to each sample in the at least one sample; and comparing the motion state detection result corresponding to each sample in the at least one sample with the motion state detection result corresponding to the sample, determining whether the initial neural network reaches a preset optimization target according to the comparison result, and taking the initial neural network as a trained motion state detection model in response to determining that the initial neural network reaches the optimization target.
In some embodiments, the step of training the motion state detection model further includes: and responding to the determination that the initial neural network does not meet the optimization target, adjusting network parameters of the initial neural network, forming a sample set by using unused samples, and continuously executing the training step by using the adjusted initial neural network as the initial neural network.
In some embodiments, prior to acquiring the target image comprising the image of the face of the human body, the method further comprises: receiving a video sent by image acquisition equipment in communication connection, wherein the video is acquired by the image acquisition equipment; and acquiring a video frame from the video as a target image.
In some embodiments, the motion state detection result comprises at least one of: excessive movement state and normal movement state.
In some embodiments, the facial state corresponding to the hyperkinetic state comprises at least one of: whitish and purple face.
In a second aspect, an embodiment of the present application provides an apparatus for motion state detection, where the apparatus includes: an acquisition unit configured to acquire a target image including a face image of a human body; the input unit is configured to input the target image into a pre-trained motion state detection model to obtain a motion state detection result of the target image, wherein the motion state detection model is used for representing a corresponding relation between an image comprising a human face image and the motion state detection result of the image; and the determining unit is configured to determine the user information corresponding to the human face image in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state.
In some embodiments, the apparatus further comprises: and the sending unit is configured to send prompt information representing the excessive movement state to the target client, wherein the prompt information comprises the determined user information.
In some embodiments, the motion state detection model is trained by: acquiring a sample set, wherein the sample comprises a sample image of a human face image and a sample motion state detection result corresponding to the sample image; performing the following training steps based on the sample set: respectively inputting a sample image of at least one sample in a sample set to an initial neural network to obtain a motion state detection result corresponding to each sample in the at least one sample; and comparing the motion state detection result corresponding to each sample in the at least one sample with the motion state detection result corresponding to the sample, determining whether the initial neural network reaches a preset optimization target according to the comparison result, and taking the initial neural network as a trained motion state detection model in response to determining that the initial neural network reaches the optimization target.
In some embodiments, the step of training the motion state detection model further includes: and responding to the determination that the initial neural network does not meet the optimization target, adjusting network parameters of the initial neural network, forming a sample set by using unused samples, and continuously executing the training step by using the adjusted initial neural network as the initial neural network.
In some embodiments, the apparatus further comprises: a receiving unit configured to receive a video transmitted by a communicatively connected image capturing device, wherein the video is captured by the image capturing device; a first acquisition unit configured to acquire a video frame from the video as a target image.
In some embodiments, the motion state detection result comprises at least one of: excessive movement state and normal movement state.
In some embodiments, the facial state corresponding to the hyperkinetic state comprises at least one of: whitish and purple face.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for detecting the motion state, firstly, a target image comprising a human face image is obtained. Then, the target image is input to a pre-trained motion state detection model, and a motion state detection result of the target image is obtained. The motion state detection model is used for representing the corresponding relation between the image comprising the human face image and the motion state detection result of the image. And finally, in response to the fact that the motion state represented by the motion state detection result belongs to the excessive motion state, determining user information corresponding to the human face image. The method and the device for detecting the motion state, provided by the embodiment of the application, can obtain the user information corresponding to the user in the excessive motion state. For example, if the over-run condition is an athlete over-run condition, the identity information corresponding to the athlete in the over-run condition may be obtained. The face detection method can be widely applied, and the cost cannot be increased along with the increase of the number of people.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram to which embodiments of the present application may be applied;
FIG. 2 is a flow diagram for one embodiment of a method for motion state detection according to the present application;
FIG. 3 is a schematic diagram of an application scenario of a method for motion state detection according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method for motion state detection according to the present application;
FIG. 5 is a schematic diagram of an embodiment of an apparatus for motion state detection according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 illustrates an exemplary system architecture 100 to which the method for motion state detection or the apparatus for motion state detection of embodiments of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include image acquisition devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the image capturing devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The image capturing devices 101, 102, 103 may interact with a server 105 via a network 104 to receive instructions or to send messages or the like. The image capture devices 101, 102, 103 may include cameras and depth cameras.
The server 105 may be a server that provides various services, such as a background server that analyzes information transmitted by the image capturing apparatuses 101, 102, and 103. The background server may monitor the target image by using the image capturing devices 101, 102, and 103, and may store the target image. The server 105 may perform processing such as analysis on the acquired data of a target image including a human face image, and determine user information corresponding to the human face in the target image.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for processing information provided in the embodiment of the present application is generally performed by the server 105, and accordingly, the apparatus for processing information is generally disposed in the server 105.
It should also be noted that the image capturing devices 101, 102, 103 may also have an image processing function. At this point, the exemplary system architecture 100 may not have the server 105 and the network 104. At this time, the method for detecting a motion state provided by the embodiment of the present application is performed by the image capturing devices 101, 102, 103, and accordingly, the device for detecting a motion state is provided in the image capturing devices 101, 102, 103.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for motion state detection according to the present application is shown. The method for motion state detection comprises the following steps:
In the present embodiment, an execution subject (e.g., the server 105 shown in fig. 1) of the method for motion state detection may acquire a target image including an image of a face of a human body. Wherein the target image is, for example, an image of a player, and the face image of a human body is, for example, an image of a face of a player. The image capturing device may be one or more electronic devices for capturing images, such as a camera, a depth camera, and the like.
It should be noted that the executing body may acquire the target image in various manners. As an example, the image may be captured by a camera installed in a sports field, and the camera is communicatively connected to the execution main body. The execution main body can monitor the players in the sports field through the camera. As yet another example, a camera may be installed in a gym or gym with its lens facing the player position in order to capture video of the player's athletic performance. Here, the camera may capture a video of a motion process of the athlete in a current time period in real time, and transmit the captured video to the execution main body in real time. The execution subject acquires a video frame from the received video as a target image.
In this embodiment, based on the target image acquired in step 201, the executing subject may input the target image to a motion state detection model trained in advance, so as to obtain a motion state detection result of the target image. The motion state detection result may include excessive motion or normal motion. For example, when the athlete has a whitish face, a purple face, etc. during the exercise, it indicates that the athlete is in an over-exercise state.
In the present embodiment, the motion state detection model is used to characterize the correspondence between an image including a human face image and the motion state detection result of the image. The execution subject may train out a motion state detection model that may represent a correspondence between an image including a human face image and a motion state detection result of the image in various ways.
As an example, the execution subject may generate a correspondence table storing a plurality of correspondences between images including a face image of the athlete and the athletic performance of the athlete based on statistics on a large number of images including a face image of the athlete and the athletic performance of the athlete, and use the correspondence table as the athletic performance detection model. In this way, the executing body may sequentially compare the target image with a plurality of expression states including facial images of the athlete in the correspondence table, and if the expression state of one image in the correspondence table is the same as or similar to the expression state of the facial image in the target image, the motion state of the athlete corresponding to the image in the correspondence table is taken as the motion state of the target image.
In this embodiment, the motion state detection model may be an artificial neural network, which abstracts a human brain neuron network from an information processing perspective, establishes a simple model, and forms different networks according to different connection modes. Usually, the system is composed of a large number of nodes (or neurons) connected to each other, each node representing a specific output function, called a stimulus function. The connection between each two nodes represents a weighted value, called weight (also called parameter), for the signal passing through the connection, and the output of the network varies according to the connection mode, the weight value and the excitation function of the network. The motion state detection model generally includes a plurality of layers, each layer includes a plurality of nodes, and generally, the weights of the nodes in the same layer may be the same, and the weights of the nodes in different layers may be different, so the parameters of the plurality of layers of the motion state detection model may also be different. Here, the execution body may input the target image from an input side of the motion state detection model, sequentially undergo processing (for example, multiplication, convolution, or the like) of parameters of each layer in the motion state detection model, and output from an output side of the motion state detection model, information output from the output side being a motion state detection result of the target image.
As another example, the executive may first obtain a plurality of sample images including a face image of the athlete and a motion state result of the sample athlete corresponding to each of the plurality of sample images; and then at least one sample image in the plurality of sample images is used as input, the motion state result of the sample athlete corresponding to the at least one sample image in the plurality of sample images is used as expected output, and the motion state detection model is obtained through training. Here, the execution subject may acquire a plurality of sample images including face images of the athlete and present them to the technician, who may empirically label each of the plurality of sample images with the motion state results of the sample athlete. The exercise state detection model may be an initial exercise state detection model, the initial exercise state detection model may be an untrained exercise state detection model or an untrained exercise state detection model, each layer of the initial exercise state detection model may be provided with initial parameters, and the parameters may be continuously adjusted during the exercise state detection model training process. The initialized motion state detection model can be various types of untrained or untrained artificial neural networks or a model obtained by combining various types of untrained or untrained artificial neural networks, for example, the initialized motion state detection model can be an untrained convolutional neural network, an untrained cyclic neural network, or a model obtained by combining an untrained convolutional neural network, an untrained cyclic neural network and an untrained full connectivity layer. In this way, the execution body can input the target video from the input side of the motion state detection model, sequentially process the parameters of each layer in the motion state detection model, and output the target video from the output side of the motion state detection model, wherein the information output by the output side is the motion state result of the target player.
In some optional implementations, the exercise state detection model may be obtained by training the execution subject or other execution subjects used for training the exercise state detection model by:
in step S1, a sample set may be obtained, where the sample may include a sample image of a face image of a human body, and a sample motion state detection result corresponding to the sample image.
Step S2, the following training steps may be performed based on the sample set: first, a sample image of at least one sample in the sample set may be respectively input to the initial neural network, so as to obtain a motion state detection result corresponding to each sample in the at least one sample. The initial neural network may be various neural networks capable of obtaining a motion state detection result from an image including a human face image, for example, a convolutional neural network, a deep neural network, or the like. Next, the motion state detection result corresponding to each sample of the at least one sample may be compared with the corresponding sample motion state detection result. And then, determining whether the initial neural network reaches a preset optimization target according to the comparison result. The comparison result refers to a set of comparison results. As an example, a motion state detection result is considered to be accurate when a difference between the motion state detection result corresponding to one sample and the motion state detection result corresponding to the sample is less than a preset difference threshold. At this time, the optimization target may refer to that the accuracy of the motion state detection result generated by the initial neural network is greater than a preset accuracy threshold. Finally, in response to determining that the initial neural network achieves the above-described optimization goal, the initial neural network may be used as a trained motion state detection model.
Optionally, the step of training the motion state detection model may further include the following steps:
in response to determining that the initial neural network does not meet the optimization goal, network parameters of the initial neural network may be adjusted, and the training step continues using unused samples to form a sample set, step S3. As an example, a Back propagation Algorithm (BP Algorithm) and a gradient descent method (e.g., a small batch gradient descent Algorithm) may be used to adjust the network parameters of the initial neural network. It should be noted that the back propagation algorithm and the gradient descent method are well-known technologies that are currently widely researched and applied, and are not described herein again.
And step 203, in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state, determining user information corresponding to the human face image.
In the present embodiment, it is determined whether the motion state represented by the motion state detection result belongs to the excessive motion state based on the motion state detection result obtained in step 202. The above-described over-exercise condition is generally referred to as an over-exercise condition. Such as a whitish complexion, etc. The above-mentioned human face image is generally referred to as a face image. Whether the motion state represented by the motion state detection result belongs to the motion excess state or not can be determined by comparing the motion state detection result with the motion excess state. The face image is subjected to face recognition, and then the user information matched with the face recognition result is searched from a preset user information database, so that the user information corresponding to the face image can be determined. User information typically refers to the identity information of the user, such as the name of the athlete, the number of the athlete, etc.
According to the method for detecting the motion state, the target image comprising the human face image is input to the pre-trained motion state detection model, so that the motion state detection result of the target image is obtained. And then, in response to the fact that the motion state represented by the motion state detection result belongs to the excessive motion state, determining user information corresponding to the human face image. A user in an over-exercise condition may be alerted. For example, if the over-exercise state is the state of over-exercise of the athlete, the athlete in the serious over-exercise state can be reminded, so as to avoid the injury to the body caused by over-exercise.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for motion state detection according to the present embodiment. In the application scenario of fig. 3, the terminal device 301 first acquires a face image 302 of the athlete. After that, the terminal device 301 may input the face image 302 into the motion state detection model 303, resulting in a motion state detection result (excessive motion) 304 of the face image. Finally, the terminal device 301 may determine the identity information 305 of the athlete corresponding to the face image in response to determining that the motion state represented by the motion state detection result 304 belongs to the excessive motion state.
The method provided by the embodiment of the application realizes detection of the target image comprising the face image, and if the motion state represented by the face image belongs to the excessive motion state, the identity information of the athlete corresponding to the face image is determined, so that the athlete in serious excessive motion can be reminded, and the injury to the body caused by excessive motion is avoided.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for motion state detection is shown. The flow 400 of the method for motion state detection comprises the steps of:
And step 403, in response to determining that the motion state represented by the motion state detection result belongs to the excessive motion state, determining user information corresponding to the human face image.
In the present embodiment, the specific operations of steps 401 and 403 are substantially the same as the operations of steps 201 and 203 in the embodiment shown in fig. 2, and are not described herein again.
And step 404, sending prompt information representing the excessive motion state to the target client.
In this embodiment, the prompt message includes the user information determined in step 403. And the execution main body sends the prompt message for recording that the user is in the excessive movement state to the target client. The above-described excessive movement state may be any set movement state, for example, an excessive movement state. The target client may be a client that needs to apply the prompt information, such as a monitoring client of a sports field, and a customer service terminal of a gymnasium.
In some optional implementations of this embodiment, before acquiring the target image including the human face image, the method further includes: receiving a video sent by image acquisition equipment in communication connection, wherein the video is acquired by the image acquisition equipment; and acquiring a video frame from the video as a target image.
In some optional implementations of this embodiment, the motion state detection result includes at least one of: excessive movement state and normal movement state.
In some optional implementations of the embodiment, the face state corresponding to the excessive-motion state includes at least one of: whitish and purple face.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for detecting a motion state in this embodiment represents a step of sending a prompt message indicating that the motion state belongs to a motion-excessive state to the target client. The prompt information is sent to the target client, so that a person using the client can be guided to perform processing, for example, the prompt information that a certain athlete is in an excessive motion state can be sent to a service person on a sports ground, and the service person can prompt the athlete to stop fierce motion so as to avoid injury. For example, a prompt message that a certain client in the gymnasium is in an excessive exercise state can be sent to a service person in the gymnasium or a terminal used by the client, so that the situation that the client continues to exercise to cause physical injury can be avoided.
With further reference to fig. 5, as an implementation of the methods shown in the above-mentioned figures, the present application provides an embodiment of an apparatus for motion state detection, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the apparatus 500 for motion state detection of the present embodiment includes: an acquisition unit 501, an input unit 502, and a determination unit 503. Wherein the acquisition unit 501 is configured to acquire a target image including an image of a face of a human body. The input unit 502 is configured to input the target image to a pre-trained motion state detection model, resulting in a motion state detection result of the target image. The motion state detection model is used for representing the corresponding relation between the image comprising the human face image and the motion state detection result of the image. The determining unit 503 is configured to determine the user information corresponding to the human face image in response to determining that the motion state represented by the motion state detection result belongs to the excessive motion state.
In this embodiment, specific processes of the obtaining unit 501, the input unit 502, and the determining unit 503 of the apparatus 500 for detecting a motion state and technical effects thereof may refer to related descriptions of step 201, step 202, and step 203 in the corresponding embodiment of fig. 2, which are not described herein again.
In some optional implementations of this embodiment, the apparatus may further include a sending unit (not shown in the figure) configured to send a prompt message indicating that the state of excessive motion belongs to the target client. Wherein the prompt message includes the determined user information.
In some optional implementations of this embodiment, the motion state detection model is obtained by training through the following steps: acquiring a sample set, wherein the sample comprises a sample image of a human face image and a sample motion state detection result corresponding to the sample image; performing the following training steps based on the sample set: respectively inputting a sample image of at least one sample in a sample set to an initial neural network to obtain a motion state detection result corresponding to each sample in the at least one sample; and comparing the motion state detection result corresponding to each sample in the at least one sample with the motion state detection result corresponding to the sample, determining whether the initial neural network reaches a preset optimization target according to the comparison result, and taking the initial neural network as a trained motion state detection model in response to determining that the initial neural network reaches the optimization target.
In some optional implementation manners of this embodiment, the step of training to obtain the motion state detection model further includes: and responding to the determination that the initial neural network does not meet the optimization target, adjusting network parameters of the initial neural network, forming a sample set by using unused samples, and continuously executing the training step by using the adjusted initial neural network as the initial neural network.
In some optional implementations of this embodiment, the apparatus further includes: a receiving unit configured to receive a video transmitted by a communicatively connected image capturing device, wherein the video is captured by the image capturing device; a first acquisition unit configured to acquire a video frame from the video as a target image.
In some optional implementations of this embodiment, the motion state detection result includes at least one of: excessive movement state and normal movement state.
In some optional implementations of the embodiment, the face state corresponding to the excessive-motion state includes at least one of: whitish and purple face.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing a server according to embodiments of the present application. The server shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, an input unit, and a determination unit. Here, the names of these units do not constitute a limitation of the unit itself in some cases, and for example, the acquisition unit may also be described as a "unit that acquires a target image including an image of a face of a human body".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring a target image comprising a human face image; inputting the target image into a pre-trained motion state detection model to obtain a motion state detection result of the target image, wherein the motion state detection model is used for representing a corresponding relation between an image comprising a human face image and the motion state detection result of the image; and determining user information corresponding to the human face image in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (16)
1. A method for motion state detection, comprising:
acquiring a target image comprising a human face image;
inputting the target image into a pre-trained motion state detection model to obtain a motion state detection result of the target image, wherein the motion state detection model is used for representing a corresponding relation between an image comprising a human face image and the motion state detection result of the image;
and in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state, determining user information corresponding to the human face image.
2. The method of claim 1, wherein the method further comprises:
and sending prompt information representing the excessive movement state to the target client, wherein the prompt information comprises the determined user information.
3. The method according to one of claims 1-2, wherein the motion state detection model is trained by:
acquiring a sample set, wherein the sample comprises a sample image of a human face image and a sample motion state detection result corresponding to the sample image;
performing the following training steps based on the sample set: respectively inputting a sample image of at least one sample in a sample set to an initial neural network to obtain a motion state detection result corresponding to each sample in the at least one sample; and comparing the motion state detection result corresponding to each sample in the at least one sample with the motion state detection result corresponding to the sample, determining whether the initial neural network reaches a preset optimization target according to the comparison result, and taking the initial neural network as a trained motion state detection model in response to determining that the initial neural network reaches the optimization target.
4. The method of claim 3, wherein the step of training the motion state detection model further comprises:
in response to determining that the initial neural network does not meet the optimization goal, adjusting network parameters of the initial neural network, and using unused samples to form a sample set, continuing the training step using the adjusted initial neural network as the initial neural network.
5. The method of claim 1, wherein prior to said acquiring a target image comprising a human face image, the method further comprises:
receiving a video sent by an image acquisition device in communication connection, wherein the video is acquired by the image acquisition device;
and acquiring a video frame from the video as a target image.
6. The method of claim 1, wherein the motion state detection result comprises at least one of: excessive movement state and normal movement state.
7. The method of claim 6, wherein the facial state corresponding to the hyperkinetic state comprises at least one of: whitish and purple face.
8. An apparatus for motion state detection, comprising:
an acquisition unit configured to acquire a target image including a face image of a human body;
the input unit is configured to input the target image into a pre-trained motion state detection model to obtain a motion state detection result of the target image, wherein the motion state detection model is used for representing a corresponding relation between an image comprising a human face image and the motion state detection result of the image;
and the determining unit is configured to determine the user information corresponding to the human face image in response to determining that the motion state represented by the motion state detection result belongs to an excessive motion state.
9. The apparatus of claim 8, wherein the apparatus further comprises:
a sending unit configured to send prompt information characterizing that the state belongs to excessive motion to a target client, wherein the prompt information includes the determined user information.
10. The apparatus according to one of claims 8-9, wherein the motion state detection model is trained by:
acquiring a sample set, wherein the sample comprises a sample image of a human face image and a sample motion state detection result corresponding to the sample image;
performing the following training steps based on the sample set: respectively inputting a sample image of at least one sample in a sample set to an initial neural network to obtain a motion state detection result corresponding to each sample in the at least one sample; and comparing the motion state detection result corresponding to each sample in the at least one sample with the motion state detection result corresponding to the sample, determining whether the initial neural network reaches a preset optimization target according to the comparison result, and taking the initial neural network as a trained motion state detection model in response to determining that the initial neural network reaches the optimization target.
11. The apparatus of claim 10, wherein the step of training the motion state detection model further comprises:
in response to determining that the initial neural network does not meet the optimization goal, adjusting network parameters of the initial neural network, and using unused samples to form a sample set, continuing the training step using the adjusted initial neural network as the initial neural network.
12. The apparatus of claim 8, wherein the apparatus further comprises:
a receiving unit configured to receive a video transmitted by a communicatively connected image capturing device, wherein the video is captured by the image capturing device;
a first acquisition unit configured to acquire a video frame from the video as a target image.
13. The apparatus of claim 8, wherein the motion state detection result comprises at least one of: excessive movement state and normal movement state.
14. The apparatus of claim 13, wherein the facial state corresponding to the hyperkinetic state comprises at least one of: whitish and purple face.
15. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080117168A1 (en) * | 2006-11-17 | 2008-05-22 | Samsung Electronics Co., Ltd. | Method and apparatus for controlling application using motion of image pickup unit |
| US20110199304A1 (en) * | 2010-02-12 | 2011-08-18 | Broadcom Corporation | Systems and Methods for Providing Enhanced Motion Detection |
| CN107945848A (en) * | 2017-11-16 | 2018-04-20 | 百度在线网络技术（北京）有限公司 | A kind of exercise guide implementation method, device, equipment and medium |
| CN108176021A (en) * | 2017-12-28 | 2018-06-19 | 必革发明（深圳）科技有限公司 | Treadmill safe early warning method, device and treadmill |
| CN108734055A (en) * | 2017-04-17 | 2018-11-02 | 杭州海康威视数字技术股份有限公司 | A kind of exception personnel detection method, apparatus and system |
| CN108805109A (en) * | 2018-08-07 | 2018-11-13 | 深圳市云康创新网络科技有限公司 | A kind of exercise data capture display system |
| CN109078318A (en) * | 2018-10-29 | 2018-12-25 | 天津市汇诚智慧体育科技有限公司 | Full crowd's outdoor body-building equipment intelligent terminal system based on big data |
| CN109192271A (en) * | 2018-09-29 | 2019-01-11 | 深圳市赛亿科技开发有限公司 | A kind of body-building management-control method and system |
-
2019
- 2019-03-05 CN CN201910164570.7A patent/CN111666782A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080117168A1 (en) * | 2006-11-17 | 2008-05-22 | Samsung Electronics Co., Ltd. | Method and apparatus for controlling application using motion of image pickup unit |
| US20110199304A1 (en) * | 2010-02-12 | 2011-08-18 | Broadcom Corporation | Systems and Methods for Providing Enhanced Motion Detection |
| CN108734055A (en) * | 2017-04-17 | 2018-11-02 | 杭州海康威视数字技术股份有限公司 | A kind of exception personnel detection method, apparatus and system |
| CN107945848A (en) * | 2017-11-16 | 2018-04-20 | 百度在线网络技术（北京）有限公司 | A kind of exercise guide implementation method, device, equipment and medium |
| CN108176021A (en) * | 2017-12-28 | 2018-06-19 | 必革发明（深圳）科技有限公司 | Treadmill safe early warning method, device and treadmill |
| CN108805109A (en) * | 2018-08-07 | 2018-11-13 | 深圳市云康创新网络科技有限公司 | A kind of exercise data capture display system |
| CN109192271A (en) * | 2018-09-29 | 2019-01-11 | 深圳市赛亿科技开发有限公司 | A kind of body-building management-control method and system |
| CN109078318A (en) * | 2018-10-29 | 2018-12-25 | 天津市汇诚智慧体育科技有限公司 | Full crowd's outdoor body-building equipment intelligent terminal system based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 潘敬奎: "人体运动识别与综合分析系统" * |
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