CN111265825A - Exercise training equipment and control method thereof - Google Patents

Exercise training equipment and control method thereof Download PDF

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Publication number
CN111265825A
CN111265825A CN202010140497.2A CN202010140497A CN111265825A CN 111265825 A CN111265825 A CN 111265825A CN 202010140497 A CN202010140497 A CN 202010140497A CN 111265825 A CN111265825 A CN 111265825A
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image
module
training
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赵立秋
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Huaian Vocational College of Information Technology
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Huaian Vocational College of Information Technology
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    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0075—Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B69/00—Training appliances or apparatus for special sports
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G06V40/23—Recognition of whole body movements, e.g. for sport training
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/30—Speed
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/806—Video cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person

Abstract

The invention belongs to the technical field of scientific training and discloses exercise training equipment and a control method thereof. The invention has simple structure, can lead the coach to master the sports training state of the athlete in real time by systematically monitoring the sports actions, the sports track and the physical conditions of the athlete, can effectively guide the coach to make a reasonable training plan for the athlete by monitoring and evaluating the sports training process of the athlete, lead the athlete to have correct training mode and training load, improve the sports result of the athlete and avoid the sports injury caused by incorrect training mode and training load. The invention can record the use condition of the sports training equipment and record and evaluate the training condition and the training effect of athletes.

Description

Exercise training equipment and control method thereof
Technical Field
The invention belongs to the technical field of scientific training, and particularly relates to exercise training equipment and a control method thereof.
Background
Currently, the closest prior art: the physical training is to give full play to the physical potential of the athletes and to improve the physical and mental qualities of the athletes to the maximum extent, thereby realizing the comprehensive development of the psychological qualities and the physical qualities of the athletes, leading the athletes to develop the whole health and developing more outstanding talents with high quality and high level for the society. However, athletes often have sudden sports injury and old secondary injury in the sports training process, and the sports injury is usually irreversible and can directly affect the career of the athletes. The main cause of the sports injury is not only largely related to training items and special technical characteristics, but also is the main cause of the sports injury without selecting correct training mode and training load.
The traditional sports training equipment has great inconvenience in maintenance and management, and mainly has the following problems: the number of times each user uses the equipment and the use condition cannot be recorded, the fatigue condition of the sports training equipment cannot be known, whether the equipment needs to be maintained and protected is difficult to predict, and the training condition and the training effect of a trainer cannot be recorded and evaluated, so that the improvement is urgently needed.
In summary, the problems of the prior art are as follows: the traditional exercise training equipment cannot record the frequency and the use condition of each user using the equipment, cannot know the fatigue condition of the exercise training equipment, is difficult to predict whether the equipment needs to be maintained and protected, and cannot record and evaluate the training condition and the training effect of a trainer.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides exercise training equipment and a control method thereof.
The present invention is achieved as such, a control method of exercise training apparatus, comprising the steps of:
acquiring the identity information of an athlete using the sports training equipment through identity information acquisition equipment; the identity information comprises the serial number, the name, the identification card number, the age, the sex, the weight, the height, the movement speed, the movement times and the movement time of the athlete; and the use information of the exercise training equipment is recorded by utilizing a counting program through the exercise sensing device.
And step two, video recording is carried out on the movement action, the movement track and the physical condition of the athlete in the movement process through a camera: (1) monitoring image acquisition is carried out on the movement action and the movement track of the athlete in the movement training by adopting three-dimensional contour reconstruction and sample pixel acquisition and adopting a Ray-Casting image characteristic scanning method;
(2) according to the image acquisition result in the step (1), carrying out information center pixel calibration and characteristic information adaptive weighting according to the physique condition monitoring image in the exercise training;
(3) solving a parallax template function of physical condition monitoring in athlete sports training, and performing sub-pixel level matching in a characteristic segmentation area of physical condition monitoring images in the athlete sports training to obtain pixel parallax of the physical condition monitoring images in the athlete sports training;
(4) initializing the spectral characteristic quantity of video monitoring, constructing a real-time monitoring model, and reconstructing the physical condition monitoring image in the image motion training by combining the three-dimensional data distribution of the athlete motion training video image three-dimensional reconstruction.
Thirdly, denoising the collected moving image through a denoising unit of the central controller: (I) the drying unit collects image original data; based on the original image data, crawling and analyzing the image triples on the network image database for the type to which the preset concept belongs;
(II) crawling a type label containing the image in a preset sub-class and adding the type label to the image triple; the preset subclass is to determine the type of the image original data to which a preset concept belongs, wherein the type of the preset concept to which the preset concept belongs comprises an image format, a size and a type;
(III) calculating the initial similarity of the image triple, adding a picture type distance to the image triple label, and acquiring the similarity of the image triple target according to the initial similarity and a preset method through searching and identifying; and performing image drying according to the similarity of the image triple target.
Fourthly, enhancing the physical condition monitoring image information in the exercise training by utilizing a gray pixel characteristic decomposition method; and extracting local dynamic feature points of the monitored image by using a self-adaptive feature extraction mode through a central controller.
Step five, analyzing the target image by using a microprocessor carried by the central controller and the analysis instructions of the preset number of sub-images and the corresponding sub-images: 1) acquiring a target image;
2) dividing the target image into a preset number of sub-images;
3) generating at least one sub-image analysis instruction according to the preset number
4) And sending the preset number of sub-images and the generated sub-image analysis instruction to the heterogeneous processor group for analysis to obtain an analysis result.
Step six, storing the collected motion training video and the collected images through a memory for later calling and watching; displaying the collected video information and the processed image through a display; power is supplied to the exercise training equipment through the external solar cell panel.
Further, in the second step, the method for determining the information center pixel calibration and the feature information adaptive weighting is as follows:
the method comprises the following steps of (1) monitoring a sparse linear equation set matched with an image template according to the physical condition in the exercise training:
g(x，y)＝h(x，y)＊f(x，y)+η(x，y)；
wherein h (x, y) is a physical condition monitoring parallax function in the athletic training of the athlete, and a symbol ＊ represents convolution, and the information center pixel calibration and the characteristic information self-adaptive weighting are carried out according to the physical condition monitoring pixel level parallax function in the athletic training of the athlete.
Further, in the second step, the pixel parallax of the distribution of the physical condition monitoring images in the sports training of the athletes is solved by using the following formula:
g(x，y)＝f(x，y)+η(x，y)；
wherein η (x, y) is the weight coefficient of the physical condition monitoring in the sports training of the athletes;
the edge pixel estimation value of the physical condition monitoring is as follows:
Figure BDA0002398910170000041
wherein F (x, y) is the pixel value of the strong texture set of the physical condition monitoring image in the sports training of the athlete relative to the scanning point (x, y), and miIs a weak texture set of the monitoring part of the physical condition,
Figure BDA0002398910170000042
for local variance, use f1(x) And f2(x) And representing the gray value of the reconstructed physical condition monitoring image in the athletic training of the athlete.
Further, in the fourth step, the method for enhancing the physical condition monitoring image information in the exercise training by using the gray pixel feature decomposition method specifically includes:
(1) establishing a lagrange function:
Figure BDA0002398910170000043
in the formula, with xiRepresents input, yiRepresenting the corresponding output, aiIs Lagrange supporter;
(2) and performing information enhancement processing on the physical condition monitoring images in the athletic training of the athletes by adopting a gray pixel characteristic decomposition method.
Further, in the fourth step, the method for extracting the local dynamic feature points of the monitored image by the central controller in the adaptive feature extraction manner is as follows:
(1) the image extraction module comprises a front-end image extraction unit, a rear-end image extraction unit and an intermediate image extraction unit which is arranged between the front-end image extraction unit and the rear-end image extraction unit in an odd number of linear forms; extracting an image sliding through the image extraction device, wherein the front-end image extraction unit extracts a front-end segment image of the object, one end of the front-end segment image is a non-overlapping area, and the other end of the front-end segment image is an overlapping area;
(2) the rear-end image extraction unit extracts a rear-end segment image of the object, wherein one end of the rear-end segment image is a non-overlapping area, and the other end of the rear-end segment image is an overlapping area; the intermediate image extraction unit respectively extracts a plurality of intermediate segment images of the object, and both ends of each intermediate segment image are overlapped areas; performing image processing to eliminate overlapping areas of the front-end segment image, the rear-end segment image and the plurality of middle segment images; and overlapping the analyzed front-end segment image, the analyzed rear-end segment image and the plurality of middle segment images into a complete image.
Further, in the fifth step, the sub-image analysis instruction carries identification information and feature analysis mode information of the sub-image to be analyzed, and processor group identification information of a heterogeneous processor group for analyzing the sub-image to be analyzed; wherein the number of the heterogeneous processor groups is at least two;
after receiving the preset number of sub-images and the generated sub-image analysis instruction, the heterogeneous processor group performs feature analysis on the target sub-images in the preset number of sub-images according to feature analysis mode information in the received sub-image analysis instruction to obtain an analysis result; and the identification information of the target sub-image is the identification information of the sub-image to be analyzed in the received sub-image analysis instruction.
Further, the heterogeneous processor group is specifically characterized as follows:
(1) each heterogeneous processor group at least comprises a Central Processing Unit (CPU); sending the preset number of sub-images and the generated sub-image analysis instruction to a heterogeneous processor group corresponding to the processor group representation information;
(2) the heterogeneous processor group corresponding to each processor group identification information also comprises a data processor; and the CPU in the heterogeneous processor group corresponding to the identification information of each processor group sends the received sub-image analysis instruction and the preset number of sub-images to the data processor in the group.
Another object of the present invention is to provide an exercise training apparatus to which the control method of the exercise training apparatus is applied, the exercise training apparatus including:
the device comprises an identity information acquisition module, a counting module, an image acquisition module, an image processing module, an image enhancement module, a central control module, an image extraction module, an image analysis module, an image storage module, a display module and a power supply module.
The identity information acquisition module is connected with the central control module and is used for acquiring the identity information of the athlete using the sports training equipment through identity information acquisition equipment;
the counting module is connected with the central control module and is used for recording the use information of the exercise training equipment by utilizing a counting program through the motion sensing device;
the image acquisition module is connected with the central control module and is used for carrying out video recording on the movement action, the movement track and the physical condition of the athlete in the movement process through the camera;
the image processing module is connected with the central control module and is used for denoising the collected moving image through a denoising unit of the central controller;
the image enhancement module is connected with the central control module and used for enhancing the physical condition monitoring image information in the exercise training by utilizing a gray pixel characteristic decomposition method;
the central control module is connected with the identity information acquisition module, the counting module, the image acquisition module, the image processing module, the image enhancement module, the image extraction module, the image analysis module, the image storage module, the display module and the power supply module and is used for controlling the normal operation of each module through the central controller;
the image extraction module is connected with the central control module and is used for extracting local dynamic feature points of the monitored image in a self-adaptive feature extraction mode;
the image analysis module is connected with the central control module and used for analyzing the target image by utilizing a microprocessor carried by the central controller through a preset number of sub-images and analysis instructions of the corresponding sub-images;
the image storage module is connected with the central control module and used for storing the collected exercise training videos and images through the storage for later calling and watching;
the display module is connected with the central control module and used for displaying the acquired video information and the processed image through a display;
and the power supply module is connected with the central control module and used for supplying power to the exercise training equipment through an external solar cell panel.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing said method of controlling an exercise training apparatus when executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the control method of the exercise training apparatus.
In summary, the advantages and positive effects of the invention are: the invention has simple structure, can lead the coach to master the sports training state of the athlete in real time by systematically monitoring the sports actions, the sports track and the physical conditions of the athlete, can effectively guide the coach to make a reasonable training plan for the athlete by monitoring and evaluating the sports training process of the athlete, improves the sports score of the athlete, leads the coach to guide and correct the problems of the athlete in the sports training process, leads the athlete to have correct training mode and training load, and avoids the sports injury caused by incorrect training mode and training load.
The invention can record the frequency and the use condition of each athlete using the equipment and can clearly know the fatigue condition of the sports training equipment, thereby predicting whether the equipment needs to be maintained and protected, greatly improving the real-time monitoring and management level of the sports training equipment and recording and evaluating the training condition and the training effect of a trainer. Meanwhile, the invention can collect the exercise data of each user, and obtain the average speed of the user during use through the exercise information of the exercise training equipment within a certain time, so that the athlete can master the exercise condition to adjust the exercise intensity of the athlete. By using the exercise training equipment, the fatigue condition of the exercise training equipment can be known through the exercise information of the exercise training equipment, so that whether maintenance and protection are needed or not is predicted.
Drawings
Fig. 1 is a flowchart of a control method of an exercise training apparatus according to an embodiment of the present invention.
FIG. 2 is a block diagram of a sports training device according to an embodiment of the present invention;
in the figure: 1. an identity information acquisition module; 2. a counting module; 3. an image acquisition module; 4. an image processing module; 5. an image enhancement module; 6. a central control module; 7. an image extraction module; 8. an image analysis module; 9. an image storage module; 10. a display module; 11. and a power supply module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems of the prior art, the present invention provides a sports training apparatus and a control method thereof, and the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, a control method of an exercise training apparatus provided in an embodiment of the present invention includes the following steps:
s101, acquiring the identity information of an athlete using the sports training equipment through identity information acquisition equipment; and recording the use information of the exercise training equipment by utilizing a counting program through the motion sensing device.
And S102, video recording is carried out on the movement motion, the movement track and the physical condition of the athlete in the movement process through the camera.
S103, denoising the collected moving image through a denoising unit of the central controller; and enhancing the physical condition monitoring image information in the exercise training by using a gray pixel characteristic decomposition method.
S104, controlling the normal operation of the exercise training equipment through a central controller; and extracting local dynamic feature points of the monitored image by using a self-adaptive feature extraction mode.
S105, analyzing the target image by using a microprocessor carried by the central controller through the preset number of sub-images and the analysis instructions of the corresponding sub-images; power is supplied to the exercise training equipment through the external solar cell panel.
S106, storing the collected motion training video and the collected images through a memory for later calling and watching; and displaying the acquired video information and the processed image through a display.
As shown in fig. 2, the exercise training apparatus provided in the embodiment of the present invention includes: the system comprises an identity information acquisition module 1, a counting module 2, an image acquisition module 3, an image processing module 4, an image enhancement module 5, a central control module 6, an image extraction module 7, an image analysis module 8, an image storage module 9, a display module 10 and a power supply module 11.
The identity information acquisition module 1 is connected with the central control module 6 and is used for acquiring the identity information of athletes using the sports training equipment through identity information acquisition equipment; the identity information comprises the serial number, the name, the identification card number, the age, the sex, the weight, the height, the movement speed, the movement times and the movement time of the athlete;
the counting module 2 is connected with the central control module 6 and is used for recording the use information of the exercise training equipment by utilizing a counting program through the motion sensing device;
the image acquisition module 3 is connected with the central control module 6 and is used for carrying out video recording on the movement action, the movement track and the physical condition of the athlete in the movement process through the camera;
the image processing module 4 is connected with the central control module 6 and is used for denoising the collected moving image through a denoising unit of the central controller;
the image enhancement module 5 is connected with the central control module 6 and used for enhancing the physical condition monitoring image information in the exercise training by utilizing a gray pixel characteristic decomposition method;
the central control module 6 is connected with the identity information acquisition module 1, the counting module 2, the image acquisition module 3, the image processing module 4, the image enhancement module 5, the image extraction module 7, the image analysis module 8, the image storage module 9, the display module 10 and the power supply module 11 and is used for controlling the normal operation of each module through a central controller;
the image extraction module 7 is connected with the central control module 6 and is used for extracting local dynamic feature points of the monitored image in a self-adaptive feature extraction mode;
the image analysis module 8 is connected with the central control module 6 and used for analyzing the target image by utilizing a microprocessor carried by the central controller through a preset number of sub-images and analysis instructions of the corresponding sub-images;
the image storage module 9 is connected with the central control module 6 and used for storing the collected exercise training videos and images through a memory for later calling and watching;
the display module 10 is connected with the central control module 6 and used for displaying the acquired video information and the processed image through a display;
and the power supply module 11 is connected with the central control module 6 and used for supplying power to the exercise training equipment through an external solar cell panel.
The invention is further described with reference to specific examples.
Example 1
As shown in fig. 1, a control method of a sports training device according to an embodiment of the present invention is a preferred embodiment, and a method for video recording of a motion action, a motion trajectory, and a physical condition of an athlete during a sport process by using a camera according to an embodiment of the present invention includes:
(1) and monitoring image acquisition is carried out on the movement action and the movement track of the athlete in the movement training by adopting three-dimensional contour reconstruction and sample pixel acquisition and adopting a Ray-Casting image characteristic scanning method.
(2) According to the result of the image acquisition, a sparse linear equation set matched with the image template is monitored according to the physical condition in the exercise training:
g(x，y)＝h(x，y)＊f(x，y)+η(x，y)；
wherein h (x, y) is a physical condition monitoring parallax function in the athletic training of the athlete, and a symbol ＊ represents convolution, and the information center pixel calibration and the characteristic information adaptive weighting are carried out according to the physical condition monitoring pixel level parallax function in the athletic training of the athlete.
(3) Solving a parallax template function of physical condition monitoring in athlete sports training, and performing sub-pixel level matching on a characteristic segmentation area of physical condition monitoring images in infant sports training to obtain pixel parallax of the physical condition monitoring images in the athlete sports training, wherein the pixel parallax is as follows:
g(x，y)＝f(x，y)+η(x，y)；
wherein η (x, y) is the weight coefficient of the physical condition monitoring in the sports training of the athlete.
The edge pixel estimation value of the physical condition monitoring is as follows:
Figure BDA0002398910170000111
wherein F (x, y) is the pixel value of the strong texture set of the physical condition monitoring image in the sports training of the athlete relative to the scanning point (x, y), and miIs a weak texture set of the monitoring part of the physical condition,
Figure BDA0002398910170000112
for local variance, use f1(x) And f2(x) The method comprises the steps of representing a gray value of the constitution status monitoring image reconstruction in the athletic training of the athlete, initializing spectral characteristic quantity of video monitoring, constructing a real-time monitoring model, and performing constitution status monitoring image reconstruction processing in the athletic training of the athlete by combining three-dimensional data distribution of three-dimensional reconstruction of the athletic training video image.
Example 2
Fig. 1 shows a control method of a sports training device according to an embodiment of the present invention, and as a preferred embodiment, the method for denoising an acquired moving image by a denoising unit of a central controller according to the embodiment of the present invention includes:
(I) the drying unit collects image original data; and based on the original image data, crawling and analyzing the image triples on the network image database according to the type to which the preset concept belongs.
(II) crawling a type label containing the image in a preset sub-class and adding the type label to the image triple; the preset subclass determines the type of the preset concept attached to the image original data, wherein the type of the preset concept attached to the image original data comprises an image format, a size and a type.
(III) calculating the initial similarity of the image triples; adding a picture type distance to the image triple tag, and acquiring the similarity of the image triple target according to the initial similarity and a preset method through searching and identifying; and performing image drying according to the similarity of the image triple target.
Example 3
As shown in fig. 1, a control method of a sports training device according to an embodiment of the present invention is, as a preferred embodiment, a method for enhancing physical condition monitoring image information in sports training by using a gray-scale pixel feature decomposition method according to an embodiment of the present invention, including:
(a) establishing a lagrange function:
Figure BDA0002398910170000121
in the formula, with xiRepresents input, yiRepresenting the corresponding output, aiIs a lagrange bearer.
(b) And performing information enhancement processing on the physical condition monitoring images in the athletic training of the athletes by adopting a gray pixel characteristic decomposition method.
Example 4
Fig. 1 shows a control method of a sports training device according to an embodiment of the present invention, and as a preferred embodiment, the method for extracting local dynamic feature points of a monitored image by using an adaptive feature extraction method according to the embodiment of the present invention includes the following specific steps:
a) the image extraction module comprises a front-end image extraction unit, a rear-end image extraction unit and an intermediate image extraction unit which is arranged between the front-end image extraction unit and the rear-end image extraction unit in an odd number of linear forms; and extracting the image sliding through the image extraction device, wherein the front-end image extraction unit extracts a front-end segment image of the object, one end of the front-end segment image is a non-overlapping area, and the other end of the front-end segment image is an overlapping area.
b) The rear-end image extraction unit extracts a rear-end segment image of the object, wherein one end of the rear-end segment image is a non-overlapping area, and the other end of the rear-end segment image is an overlapping area; the intermediate image extraction unit respectively extracts a plurality of intermediate segment images of the object, and both ends of each intermediate segment image are overlapped areas; performing image processing to eliminate overlapping areas of the front-end segment image, the rear-end segment image and the plurality of middle segment images; and overlapping the analyzed front-end segment image, the analyzed rear-end segment image and the plurality of middle segment images into a complete image.
Example 5
Fig. 1 shows a control method of a sports training device according to an embodiment of the present invention, and as a preferred embodiment, a method for analyzing a target image by using a microprocessor mounted in a central controller according to an embodiment of the present invention with a preset number of sub-images and analysis instructions of the corresponding sub-images includes:
1) and acquiring a target image.
2) And dividing the target image into a preset number of sub-images.
3) Generating at least one sub-image analysis instruction according to the preset number; the sub-image analysis instruction carries identification information and feature analysis mode information of a sub-image to be analyzed, and processor group identification information of a heterogeneous processor group for analyzing the sub-image to be analyzed; wherein the number of the heterogeneous processor groups is at least two.
4) Sending the preset number of sub-images and the generated sub-image analysis instruction to a heterogeneous processor group corresponding to the processor group identification information, so that the heterogeneous processor group performs feature analysis on target sub-images in the preset number of sub-images according to feature analysis mode information in the received sub-image analysis instruction to obtain an analysis result; and the identification information of the target sub-image is the identification information of the sub-image to be analyzed in the received sub-image analysis instruction.
The heterogeneous processor group provided by the embodiment of the invention has the following characteristics:
each heterogeneous processor group at least comprises a Central Processing Unit (CPU); sending the preset number of sub-images and the generated sub-image analysis instruction to a heterogeneous processor group corresponding to the processor group representation information;
the heterogeneous processor group corresponding to each processor group identification information also comprises a data processor; and the CPU in the heterogeneous processor group corresponding to the identification information of each processor group sends the received sub-image analysis instruction and the preset number of sub-images to the data processor in the group.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. A control method of an exercise training apparatus, characterized by comprising the steps of:
firstly, acquiring the identity information of an athlete using the sports training equipment through identity information acquisition equipment; the identity information comprises the serial number, the name, the identification card number, the age, the sex, the weight, the height, the movement speed, the movement times and the movement time of the athlete; recording the use information of the exercise training equipment by utilizing a counting program through the exercise induction device;
and step two, video recording is carried out on the movement action, the movement track and the physical condition of the athlete in the movement process through a camera: (1) monitoring image acquisition is carried out on the movement action and the movement track of the athlete in the movement training by adopting three-dimensional contour reconstruction and sample pixel acquisition and adopting a Ray-Casting image characteristic scanning method;
(2) according to the image acquisition result in the step (1), carrying out information center pixel calibration and characteristic information adaptive weighting according to the physique condition monitoring image in the exercise training;
(3) solving a parallax template function of physical condition monitoring in athlete sports training, and performing sub-pixel level matching in a characteristic segmentation area of physical condition monitoring images in the athlete sports training to obtain pixel parallax of the physical condition monitoring images in the athlete sports training;
(4) initializing spectral characteristic quantity of video monitoring, constructing a real-time monitoring model, and reconstructing a physical condition monitoring image in image motion training by combining three-dimensional data distribution of three-dimensional reconstruction of a motion training video image of an athlete;
thirdly, denoising the collected moving image through a denoising unit of the central controller: (I) the drying unit collects image original data; based on the original image data, crawling and analyzing the image triples on the network image database for the type to which the preset concept belongs;
(II) crawling a type label containing the image in a preset sub-class and adding the type label to the image triple; the preset subclass is to determine the type of the image original data to which a preset concept belongs, wherein the type of the preset concept to which the preset concept belongs comprises an image format, a size and a type;
(III) calculating the initial similarity of the image triple, adding a picture type distance to the image triple label, and acquiring the similarity of the image triple target according to the initial similarity and a preset method through searching and identifying; according to the similarity of the image triple targets, performing image drying;
fourthly, enhancing the physical condition monitoring image information in the exercise training by utilizing a gray pixel characteristic decomposition method; extracting local dynamic feature points of the monitored image by a central controller in a self-adaptive feature extraction mode;
step five, analyzing the target image by using a microprocessor carried by the central controller and the analysis instructions of the preset number of sub-images and the corresponding sub-images: 1) acquiring a target image;
2) dividing the target image into a preset number of sub-images;
3) generating at least one sub-image analysis instruction according to the preset number
4) Sending the preset number of sub-images and the generated sub-image analysis instruction to the heterogeneous processor group for analysis to obtain an analysis result;
step six, storing the collected motion training video and the collected images through a memory for later calling and watching; displaying the collected video information and the processed image through a display; power is supplied to the exercise training equipment through the external solar cell panel.
2. The method for controlling exercise training equipment of claim 1, wherein in step two, the method for determining the pixel calibration of the information center and the adaptive weighting of the feature information is as follows:
the method comprises the following steps of (1) monitoring a sparse linear equation set matched with an image template according to the physical condition in the exercise training:
g(x，y)＝h(x，y)＊f(x，y)+η(x，y)；
wherein h (x, y) is a physical condition monitoring parallax function in the athletic training of the athlete, and a symbol ＊ represents convolution, and the information center pixel calibration and the characteristic information self-adaptive weighting are carried out according to the physical condition monitoring pixel level parallax function in the athletic training of the athlete.
3. The method for controlling exercise training equipment according to claim 1, wherein in the second step, the pixel parallax of the distribution of the physical condition monitoring images in the exercise training of the athlete is solved by using the following formula:
g(x，y)＝f(x，y)+η(x，y)；
wherein η (x, y) is the weight coefficient of the physical condition monitoring in the sports training of the athletes;
the edge pixel estimation value of the physical condition monitoring is as follows:
Figure FDA0002398910160000031
wherein F (x, y) is the pixel value of the strong texture set of the physical condition monitoring image in the sports training of the athlete relative to the scanning point (x, y), and miIs a weak texture set of the monitoring part of the physical condition,
Figure FDA0002398910160000032
for local variance, use f1(x) And f2(x) And representing the gray value of the reconstructed physical condition monitoring image in the athletic training of the athlete.
4. The method for controlling exercise training equipment according to claim 1, wherein in step four, the method for enhancing the physical condition monitoring image information in the exercise training by using the gray pixel feature decomposition method specifically comprises the following steps:
(1) establishing a lagrange function:
Figure FDA0002398910160000033
in the formula, with xiRepresents input, yiRepresenting the corresponding output, aiIs Lagrange supporter;
(2) and performing information enhancement processing on the physical condition monitoring images in the athletic training of the athletes by adopting a gray pixel characteristic decomposition method.
5. The method for controlling exercise training equipment according to claim 1, wherein in step four, the method for extracting the local dynamic feature points of the monitored images by the central controller by using the adaptive feature extraction method comprises the following steps:
(1) the image extraction module comprises a front-end image extraction unit, a rear-end image extraction unit and an intermediate image extraction unit which is arranged between the front-end image extraction unit and the rear-end image extraction unit in an odd number of linear forms; extracting an image sliding through the image extraction device, wherein the front-end image extraction unit extracts a front-end segment image of the object, one end of the front-end segment image is a non-overlapping area, and the other end of the front-end segment image is an overlapping area;
(2) the rear-end image extraction unit extracts a rear-end segment image of the object, wherein one end of the rear-end segment image is a non-overlapping area, and the other end of the rear-end segment image is an overlapping area; the intermediate image extraction unit respectively extracts a plurality of intermediate segment images of the object, and both ends of each intermediate segment image are overlapped areas; performing image processing to eliminate overlapping areas of the front-end segment image, the rear-end segment image and the plurality of middle segment images; and overlapping the analyzed front-end segment image, the analyzed rear-end segment image and the plurality of middle segment images into a complete image.
6. The method according to claim 1, wherein in step five, the sub-image analysis instruction carries identification information of a sub-image to be analyzed, feature analysis mode information, and processor group identification information of a heterogeneous processor group that analyzes the sub-image to be analyzed; wherein the number of the heterogeneous processor groups is at least two;
after receiving the preset number of sub-images and the generated sub-image analysis instruction, the heterogeneous processor group performs feature analysis on the target sub-images in the preset number of sub-images according to feature analysis mode information in the received sub-image analysis instruction to obtain an analysis result; and the identification information of the target sub-image is the identification information of the sub-image to be analyzed in the received sub-image analysis instruction.
7. The method of controlling an exercise training device of claim 6, wherein the set of heterogeneous processors is characterized by the following:
(1) each heterogeneous processor group at least comprises a Central Processing Unit (CPU); sending the preset number of sub-images and the generated sub-image analysis instruction to a heterogeneous processor group corresponding to the processor group representation information;
(2) the heterogeneous processor group corresponding to each processor group identification information also comprises a data processor; and the CPU in the heterogeneous processor group corresponding to the identification information of each processor group sends the received sub-image analysis instruction and the preset number of sub-images to the data processor in the group.
8. An exercise training apparatus to which the control method of an exercise training apparatus according to any one of claims 1 to 7 is applied, characterized by comprising:
the system comprises an identity information acquisition module, a counting module, an image acquisition module, an image processing module, an image enhancement module, a central control module, an image extraction module, an image analysis module, an image storage module, a display module and a power supply module;
the identity information acquisition module is connected with the central control module and is used for acquiring the identity information of the athlete using the sports training equipment through identity information acquisition equipment;
the counting module is connected with the central control module and is used for recording the use information of the exercise training equipment by utilizing a counting program through the motion sensing device;
the image acquisition module is connected with the central control module and is used for carrying out video recording on the movement action, the movement track and the physical condition of the athlete in the movement process through the camera;
the image processing module is connected with the central control module and is used for denoising the collected moving image through a denoising unit of the central controller;
the image enhancement module is connected with the central control module and used for enhancing the physical condition monitoring image information in the exercise training by utilizing a gray pixel characteristic decomposition method;
the central control module is connected with the identity information acquisition module, the counting module, the image acquisition module, the image processing module, the image enhancement module, the image extraction module, the image analysis module, the image storage module, the display module and the power supply module and is used for controlling the normal operation of each module through the central controller;
the image extraction module is connected with the central control module and is used for extracting local dynamic feature points of the monitored image in a self-adaptive feature extraction mode;
the image analysis module is connected with the central control module and used for analyzing the target image by utilizing a microprocessor carried by the central controller through a preset number of sub-images and analysis instructions of the corresponding sub-images;
the image storage module is connected with the central control module and used for storing the collected exercise training videos and images through the storage for later calling and watching;
the display module is connected with the central control module and used for displaying the acquired video information and the processed image through a display;
and the power supply module is connected with the central control module and used for supplying power to the exercise training equipment through an external solar cell panel.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing a method of controlling an exercise training apparatus as claimed in any one of claims 1 to 7 when executed on an electronic device.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method of controlling an exercise training apparatus according to any one of claims 1 to 7.
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