CN116744102B - Ball machine tracking method and device based on feedback adjustment - Google Patents
Ball machine tracking method and device based on feedback adjustment Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/61—Control of cameras or camera modules based on recognised objects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/66—Remote control of cameras or camera parts, e.g. by remote control devices
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
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Abstract
The invention discloses a ball machine tracking method and device based on feedback adjustment. Wherein the method comprises the following steps: acquiring real-time picture data; performing target recognition on the real-time picture data according to a target recognition model to obtain a recognition result; analyzing the motion parameters in the identification result to obtain a motion speed and a motion direction; and generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and carrying out the ball machine tracking operation according to the expected parameters of the motion. The invention solves the technical problems that in the prior art, the detection of the target and the (single) target tracking cannot be performed on a complex scene accurately, and the PTZ control (comprising direction, zoom and speed) of the dome camera cannot be utilized to optimize the whole feedback system, so that smooth and stable tracking is difficult.
Description
Technical Field
The invention relates to the field of camera equipment tracking, in particular to a ball machine tracking method and device based on feedback adjustment.
Background
Along with the continuous development of intelligent science and technology, intelligent equipment is increasingly used in life, work and study of people, and the quality of life of people is improved and the learning and working efficiency of people is increased by using intelligent science and technology means.
At present, when application scene monitoring is performed, a ball machine under a default condition can fixedly monitor a certain position or automatically cruises according to a certain path, and when the ball machine detects a target (such as an airplane, a ship, a vehicle, a pedestrian and the like), automatic tracking is started until the target is lost, and then the ball machine is restored to a default state. However, in the prior art, the detection of the target and the (single) target tracking cannot be performed on a complex scene accurately, and the whole feedback system cannot be optimized by using the PTZ control (including direction, zoom and speed) of the dome camera, so that smooth and stable tracking is difficult.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a ball machine tracking method and device based on feedback adjustment, which at least solve the technical problems that in the prior art, the detection of a target and the (single) target tracking cannot accurately track a complex scene, and meanwhile, the PTZ control (including direction, scaling and speed) of a ball machine cannot be utilized to optimize the whole feedback system, so that smooth and stable tracking is difficult to achieve.
According to an aspect of the embodiment of the present invention, there is provided a ball machine tracking method based on feedback adjustment, including: acquiring real-time picture data; performing target recognition on the real-time picture data according to a target recognition model to obtain a recognition result; analyzing the motion parameters in the identification result to obtain a motion speed and a motion direction; and generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and carrying out the ball machine tracking operation according to the expected parameters of the motion.
Optionally, before the target recognition is performed on the real-time picture data according to the target recognition model to obtain a recognition result, the method further includes: training the target recognition model through historical recognition data.
Optionally, the analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction includes: collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty.
Optionally, the generating the expected parameters of the ball machine according to the preset advance threshold, the motion speed and the motion direction, and performing the tracking operation of the ball machine according to the expected parameters of the ball machine includes: according to the presetAdvance threshold, speed and direction of motion, using the formulaCalculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and carrying out the tracking operation of the ball machine according to the motion expected parameters.
According to another aspect of the embodiment of the present invention, there is also provided a ball machine tracking device based on feedback adjustment, including: the acquisition module is used for acquiring real-time picture data; the identification module is used for carrying out target identification on the real-time picture data according to a target identification model to obtain an identification result; the analysis module is used for analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction; the generation module is used for generating expected parameters of the ball machine motion according to a preset advance threshold value, the motion speed and the motion direction, and carrying out the ball machine tracking operation according to the expected parameters of the motion.
Optionally, the apparatus further includes: and the training module is used for training the target recognition model through the historical recognition data.
Optionally, the analysis module includes: the acquisition unit is used for acquiring the motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; the input unit is used for inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises the following steps:
where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty.
Optionally, theThe generation module comprises: a calculation unit for using a formula according to the preset advance threshold, the movement speed and the movement directionCalculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and the tracking unit is used for carrying out the tracking operation of the ball machine according to the motion expected parameters.
According to another aspect of the embodiment of the present invention, there is further provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, the device in which the non-volatile storage medium is controlled to execute a ball machine tracking method based on feedback adjustment.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a feedback adjustment based ball machine tracking method when executed.
In the embodiment of the invention, the real-time picture data is acquired; performing target recognition on the real-time picture data according to a target recognition model to obtain a recognition result; analyzing the motion parameters in the identification result to obtain a motion speed and a motion direction; according to the method, expected parameters of the movement of the ball machine are generated according to preset advance threshold values, movement speed and movement direction, and the ball machine tracking operation is carried out according to the expected parameters of the movement, so that the technical problems that in the prior art, detection of targets and (single) target tracking cannot accurately track complex scenes, and meanwhile, the whole feedback system cannot be optimized by PTZ control (comprising direction, scaling and speed) of the ball machine are difficult to track smoothly and stably are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a feedback adjustment based ball machine tracking method according to an embodiment of the invention;
FIG. 2 is a block diagram of a feedback adjustment based ball machine tracking device in accordance with an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing the method according to the invention according to an embodiment of the invention;
fig. 4 is a memory unit for holding or carrying program code for implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a feedback adjustment-based ball machine tracking method, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that, although a logical sequence is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in a different order than that illustrated herein.
Example 1
Fig. 1 is a flowchart of a ball machine tracking method based on feedback adjustment according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S102, acquiring real-time picture data.
Specifically, in order to solve the technical problems that in the prior art, detection of a target and (single) target tracking cannot be performed on a complex scene accurately, and meanwhile, the whole feedback system cannot be optimized by PTZ control (including direction, zoom and speed) of a dome camera, so that smooth and stable tracking is difficult, firstly, real-time picture data are required to be acquired according to camera equipment, and the real-time picture data belong to real-time raw unprocessed image data shot by a monitoring camera and are used for identifying and tracking a target in a follow-up image.
And step S104, carrying out target recognition on the real-time picture data according to a target recognition model to obtain a recognition result.
Specifically, the embodiment of the invention needs to combine the obtained target recognition model and the real-time picture data to obtain a recognition result, wherein the target recognition model can train the DNN deep neural network model through a DNN deep neural network model by using a historical data set of a data platform, and obtain a mature target recognition model after training, and the model can take the input real-time picture data as a feature vector and output expected data.
Optionally, before the target recognition is performed on the real-time picture data according to the target recognition model to obtain a recognition result, the method further includes: training the target recognition model through historical recognition data.
And S106, analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction.
Optionally, the analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction includes: collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty.
Specifically, in order to perform feedback tracking on the image data in the subsequent processing, the feedback parameters, that is, the motion parameters of the target, need to be analyzed to obtain the relevant parameter values associated with tracking the ball machine, and if the motion parameters in the identification result are analyzed, the obtaining of the motion speed and the motion direction includes: collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty.
And S108, generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and performing the ball machine tracking operation according to the expected parameters of the motion.
Optionally, according to a preset advance threshold value,The method for generating the expected parameters of the movement of the ball machine according to the movement speed and the movement direction comprises the following steps: according to the preset advance threshold value, the movement speed and the movement direction, a formula is utilizedCalculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and carrying out the tracking operation of the ball machine according to the motion expected parameters.
Specifically, in the embodiment of the present invention, after the motion speed and the motion direction of the tracked object in the frame are obtained, in order to calculate the expected parameters of the motion of the ball machine, the expected parameters of the motion of the ball machine need to be calculated according to the preset advance threshold, for example, according to the preset advance threshold, the motion speed and the motion direction, a formula is used Calculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and carrying out the tracking operation of the ball machine according to the motion expected parameters.
Through the embodiment, the technical problems that in the prior art, detection of a target and (single) target tracking cannot be performed on a complex scene accurately, and meanwhile, the whole feedback system cannot be optimized by PTZ control (comprising direction, scaling and speed) of a dome camera are solved, so that smooth and stable tracking is difficult.
Example two
Fig. 2 is a block diagram of a ball machine tracking device based on feedback adjustment according to an embodiment of the present invention, as shown in fig. 2, the device includes:
the acquiring module 20 is configured to acquire real-time frame data.
Specifically, in order to solve the technical problems that in the prior art, detection of a target and (single) target tracking cannot be performed on a complex scene accurately, and meanwhile, the whole feedback system cannot be optimized by PTZ control (including direction, zoom and speed) of a dome camera, so that smooth and stable tracking is difficult, firstly, real-time picture data are required to be acquired according to camera equipment, and the real-time picture data belong to real-time raw unprocessed image data shot by a monitoring camera and are used for identifying and tracking a target in a follow-up image.
And the recognition module 22 is used for carrying out target recognition on the real-time picture data according to a target recognition model to obtain a recognition result.
Specifically, the embodiment of the invention needs to combine the obtained target recognition model and the real-time picture data to obtain a recognition result, wherein the target recognition model can train the DNN deep neural network model through a DNN deep neural network model by using a historical data set of a data platform, and obtain a mature target recognition model after training, and the model can take the input real-time picture data as a feature vector and output expected data.
Optionally, the apparatus further includes: and the training module is used for training the target recognition model through the historical recognition data.
And the analysis module 24 is used for analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction.
Optionally, the analysis module includes: the acquisition unit is used for acquiring the motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; the input unit is used for inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises the following steps:
where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty.
Specifically, in order to perform feedback tracking on the image data in the subsequent processing, the feedback parameters, that is, the motion parameters of the target, need to be analyzed to obtain the relevant parameter values associated with tracking the ball machine, and if the motion parameters in the identification result are analyzed, the obtaining of the motion speed and the motion direction includes: collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty.
The generating module 26 is configured to generate a desired movement parameter of the ball machine according to a preset advance threshold, a movement speed and a movement direction, and perform a tracking operation of the ball machine according to the desired movement parameter.
Optionally, the generating module includes: a calculation unit for using a formula according to the preset advance threshold, the movement speed and the movement directionCalculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; the tracking unit is used for tracking the ball machine according to the motion expected parametersAnd (3) operating.
Specifically, in the embodiment of the present invention, after the motion speed and the motion direction of the tracked object in the frame are obtained, in order to calculate the expected parameters of the motion of the ball machine, the expected parameters of the motion of the ball machine need to be calculated according to the preset advance threshold, for example, according to the preset advance threshold, the motion speed and the motion direction, a formula is used Calculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and carrying out the tracking operation of the ball machine according to the motion expected parameters.
Through the embodiment, the technical problems that in the prior art, detection of a target and (single) target tracking cannot be performed on a complex scene accurately, and meanwhile, the whole feedback system cannot be optimized by PTZ control (comprising direction, scaling and speed) of a dome camera are solved, so that smooth and stable tracking is difficult.
According to another aspect of the embodiment of the present invention, there is further provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, the device in which the non-volatile storage medium is controlled to execute a ball machine tracking method based on feedback adjustment.
Specifically, the method comprises the following steps: acquiring real-time picture data; performing target recognition on the real-time picture data according to a target recognition model to obtain a recognition result; analyzing the motion parameters in the identification result to obtain a motion speed and a motion direction; and generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and carrying out the ball machine tracking operation according to the expected parameters of the motion. Optionally, at the rootPerforming target recognition on the real-time picture data according to a target recognition model, and before a recognition result is obtained, the method further comprises: training the target recognition model through historical recognition data. Optionally, the analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction includes: collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty. Optionally, the generating the expected parameters of the ball machine according to the preset advance threshold, the motion speed and the motion direction, and performing the tracking operation of the ball machine according to the expected parameters of the ball machine includes: according to the preset advance threshold value, the movement speed and the movement direction, a formula is utilizedCalculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and carrying out the tracking operation of the ball machine according to the motion expected parameters.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a feedback adjustment based ball machine tracking method when executed.
Specifically, the method comprises the following steps: acquiring real-time picture data; performing target recognition on the real-time picture data according to a target recognition model to obtainIdentifying a result; analyzing the motion parameters in the identification result to obtain a motion speed and a motion direction; and generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and carrying out the ball machine tracking operation according to the expected parameters of the motion. Optionally, before the target recognition is performed on the real-time picture data according to the target recognition model to obtain a recognition result, the method further includes: training the target recognition model through historical recognition data. Optionally, the analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction includes: collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information; inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:where sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty. Optionally, the generating the expected parameters of the ball machine according to the preset advance threshold, the motion speed and the motion direction, and performing the tracking operation of the ball machine according to the expected parameters of the ball machine includes: according to the preset advance threshold value, the movement speed and the movement direction, a formula is utilizedCalculating the expected parameters of the movement of the spherical machine, wherein f is the expected parameters of the movement of the spherical machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, wherein the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the spherical machine; and carrying out the tracking operation of the ball machine according to the motion expected parameters.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, fig. 3 is a schematic hardware structure of a terminal device according to an embodiment of the present application. As shown in fig. 3, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable communication connections between the elements. The memory 33 may comprise a high-speed RAM memory or may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the processor 31 may be implemented as, for example, a central processing unit (Central Processing Unit, abbreviated as CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 through wired or wireless connections.
Alternatively, the input device 30 may include a variety of input devices, for example, may include at least one of a user-oriented user interface, a device-oriented device interface, a programmable interface of software, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware insertion interface (such as a USB interface, a serial port, etc.) for data transmission between devices; alternatively, the user-oriented user interface may be, for example, a user-oriented control key, a voice input device for receiving voice input, and a touch-sensitive device (e.g., a touch screen, a touch pad, etc. having touch-sensitive functionality) for receiving user touch input by a user; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, for example, an input pin interface or an input interface of a chip, etc.; optionally, the transceiver may be a radio frequency transceiver chip, a baseband processing chip, a transceiver antenna, etc. with a communication function. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, audio, or the like.
In this embodiment, the processor of the terminal device may include functions for executing each module of the data processing apparatus in each device, and specific functions and technical effects may be referred to the above embodiments and are not described herein again.
Fig. 4 is a schematic hardware structure of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of the implementation of fig. 3. As shown in fig. 4, the terminal device of the present embodiment includes a processor 41 and a memory 42.
The processor 41 executes the computer program code stored in the memory 42 to implement the methods of the above-described embodiments.
The memory 42 is configured to store various types of data to support operation at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, video, etc. The memory 42 may include a random access memory (random access memory, simply referred to as RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power supply component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The components and the like specifically included in the terminal device are set according to actual requirements, which are not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 40 may include one or more modules that facilitate interactions between the processing component 40 and other components. For example, processing component 40 may include a multimedia module to facilitate interaction between multimedia component 45 and processing component 40.
The power supply assembly 44 provides power to the various components of the terminal device. Power supply components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for terminal devices.
The multimedia component 45 comprises a display screen between the terminal device and the user providing an output interface. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation.
The audio component 46 is configured to output and/or input audio signals. For example, the audio component 46 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operational mode, such as a speech recognition mode. The received audio signals may be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 further includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing assembly 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: volume button, start button and lock button.
The sensor assembly 48 includes one or more sensors for providing status assessment of various aspects for the terminal device. For example, the sensor assembly 48 may detect the open/closed state of the terminal device, the relative positioning of the assembly, the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera or the like.
The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot, where the SIM card slot is used to insert a SIM card, so that the terminal device may log into a GPRS network, and establish communication with a server through the internet.
From the above, it will be appreciated that the communication component 43, the audio component 46, and the input/output interface 47, the sensor component 48 referred to in the embodiment of fig. 4 may be implemented as an input device in the embodiment of fig. 3.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (4)
1. The ball machine tracking method based on feedback adjustment is characterized by comprising the following steps of:
acquiring real-time picture data;
performing target recognition on the real-time picture data according to a target recognition model to obtain a recognition result;
analyzing the motion parameters in the identification result to obtain a motion speed and a motion direction;
generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and carrying out the ball machine tracking operation according to the expected parameters of the motion;
before the target recognition is carried out on the real-time picture data according to the target recognition model to obtain a recognition result, the method further comprises the following steps:
training the target recognition model through historical recognition data;
analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction comprises the following steps:
collecting motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information;
inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises:
wherein sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty;
the generating the expected parameters of the ball machine motion according to the preset advance threshold, the motion speed and the motion direction, and the performing the ball machine tracking operation according to the expected parameters of the ball machine motion comprises:
according to the preset advance threshold value, the movement speed and the movement direction, a formula is utilized
Calculating the expected parameters of the movement of the ball machine, wherein f is the expected parameters of the movement of the ball machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, and the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the ball machine;
and carrying out the tracking operation of the ball machine according to the motion expected parameters.
2. A ball machine tracking device based on feedback adjustment, comprising:
the acquisition module is used for acquiring real-time picture data;
the identification module is used for carrying out target identification on the real-time picture data according to a target identification model to obtain an identification result;
the analysis module is used for analyzing the motion parameters in the identification result to obtain the motion speed and the motion direction;
the generation module is used for generating expected parameters of the ball machine motion according to a preset advance threshold value, a motion speed and a motion direction, and carrying out the tracking operation of the ball machine according to the expected parameters of the motion;
the apparatus further comprises:
the training module is used for training the target recognition model through the historical recognition data;
the analysis module comprises:
the acquisition unit is used for acquiring the motion parameters in the identification result, wherein the motion parameters comprise: motion path, motion time information;
the input unit is used for inputting the motion parameters into a preset algorithm to obtain the motion speed and the motion direction, wherein the preset algorithm comprises the following steps:
wherein sinA is a motion velocity vector, sinB is a motion direction vector, t is motion time information, s is a motion path, and α is uncertainty;
the generation module comprises:
a calculation unit for using a formula according to the preset advance threshold, the movement speed and the movement direction
Calculating the expected parameters of the movement of the ball machine, wherein f is the expected parameters of the movement of the ball machine, x is a movement mark index, sinA is a movement speed vector, sinB is a movement direction vector, and delta is a preset advance threshold, and the preset advance threshold is used for representing the parameters of the degree of advance of the sensitivity of the ball machine;
and the tracking unit is used for carrying out the tracking operation of the ball machine according to the motion expected parameters.
3. A non-volatile storage medium comprising a stored program, wherein the program when run controls a device in which the non-volatile storage medium resides to perform the method of claim 1.
4. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of claim 1.
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