# Application of Moving Target Information Perception Technology in Intelligent Supervision System

Authors: Mingjiang Zhu
Journal: Scanning
 (2022)

---

## Abstract

In order to solve the problems of inaccurate information collection, incomplete information collection, and inconsistency of collected images in traditional sports injury collection methods, an application method of moving target information perception technology in intelligent supervision system is proposed. By judging and analyzing the potential motion damage posture of the motion posture intelligent tracking images, the collected motion intelligence tracking images are judged. The intelligent tracking image matrix can make up for the shortcomings of traditional images that are not connected, complete the identification, detect potential damage in time, and take targeted preventive measures and means. Finally, according to the target detection algorithm and target tracking algorithm, combined with OpenCV computer vision library and QT image library, an intelligent video surveillance target tracking simulation system is developed. The algorithm studied in this paper is to realize the target tracking function of the intelligent video surveillance system. Through the comparison of experimental results, the design method can accurately collect damage attitude information, without calculating continuous values, and the use of three-dimensional images in the positioning process can analyze the damage attitude from multiple angles.


---

## Body

## 1. Introduction

Athletes’ mistakes during daily competitions and training can ultimately lead to injuries. In addition to introducing modern science and technology to sports, some research and technology has been used to identify and document the specifics of sports in order to avoid injuries to athletes in the sports process [1]. Procedures typically record the physical data of a sports injury process by limiting physical exercise, imaging, and image counting systems. Data obtained from known fault locations by differential and data outputs are error data and offset data, including method modeling, some errors, and data limits. The accurate analysis and prediction of human motion posture can provide effective data support for sports training. By obtaining the relevant data of human movement and correcting the details of athletes’ actions in combination with the standard database data, we can improve the athletes’ sports level [2]. An intelligent multiple-object tracking system adopts advanced image detection, recognition, and tracking technology and cooperates with a precision motion control system to realize continuous and rapid tracking and capture of multiple moving targets in large scenes, as shown in Figure 1. It integrates the multitarget tracking and detection and video analysis functions in a large scene into an independent system. Through the intelligent analysis of the video information collected by the front-end camera, it automatically collects and classifies the abnormal behavior and events and linkage alarm. At the same time, the background can see the analysis data and record videos in real time, and video extraction and forensics can be carried out through event retrieval [3]. One of the biggest advantages of the system is that it can identify and monitor different behavior patterns of multiple targets in the same scene at the same time. It can be widely used in various large public places, including airports, stations, prisons, ports, mines, oil fields, nursing homes, streets, communities, shopping malls, and other important places, which are used to detect, classify, track, and record passing pedestrians, vehicles, and other suspicious objects, to judge whether there is abnormal behavior and give an alarm.Figure 1
Intelligent multitarget tracking system.


## 2. Literature Review

Son and others now believe that all personal activities are active and rapidly evolving, that human health is gradually expanding, and that athletes’ feelings of injury are becoming more common. As the importance of concealing the dangers of sports grows, so does the demand for sports to prevent injuries [4]. Kadupitiya and others have suggested that this paper consider the relationship between mental health factors such as risk and decision-making in terms of mental sports. Kadupitiya et al. indicated that from the perspective of sports psychology, the correlation between psychological predictors such as risk cognition, risk taking, exercise ability, sports injury experience, and sports injury in the mass fitness population was explored. The above cases of sports injuries and their gender differences with real physical effort are studied in order to establish a theoretical basis for the reduction and prevention of sports injuries [5]. Studies by Xu and others on the sports injury location platform at this stage have shown to some extent the impact of sports injuries, and athletes believe that they protect themselves and their coaches from injury and other professionals [6]. Rohei and others believe that sports injury prevention courses are only for professionals in the field of sports. As each sport differs in stats and defenses, athletes cannot identify injuries, causing problems for defenses, protection of athletes, resulting in recovery, wasted resources, and reduced efficiency [7]. Assaqty and others argue that the ability to prevent sports injuries is widely used in major sports. Based on the new prevention strategy, prevention strategies are developed for all injuries on the sports field, examining and implementing complex procedures and interventions involving injuries and injury data. The complex and closely related damage data information can be analyzed and processed efficiently and conveniently [8]. Moradi and Ehsanian believed that the risk factors for sports injuries at this stage are the wrists, hips, and knees. Most of them are incarcerated, usually chronic, which poses a serious risk to the health of athletes [9]. Prasad et al. stated that the biggest problem in physical examination may be the injury of athletes’ daily energy and two-dimensional imaging technology, such as wrists, hips, and knees and other parts which cannot be guaranteed, and changes in injury cannot be observed intuitively. Internal distribution of internal information on the importance of movement cannot be distributed efficiently. As a result, the errors and distortions in the fine posture analysis of these parts are large and cannot be combined with clinical practice [10]. Kwak and others said that with the rapid development of computer vision, the intelligent technology of video surveillance system has also made some progress, and the research on target tracking method for intelligent video surveillance system has become a hot spot in computer vision-related professional research [11]. Pathak and others believe that target tracking is the core function of the intelligent video surveillance system and the basis of intelligent target surveillance system for target recognition, behavior analysis, and other work, which has important research value [12].


## 3. Method

### 3.1. Image Denoising

Noise refers to the factors that cause adverse effects in the processing or analysis of images. In most cases, the noise is a random signal and unpredictable, but it can be mathematically described by the method of probability and statistics. Noise comes from image acquisition, transmission, compression, and other links, in which the acquisition stage is the most serious link. When the noise is too large, it will affect the results of image processing and analysis and produce large errors [13]. Therefore, the first step of image analysis is to denoise the image, suppress the influence of noise, and improve the processing accuracy. With the continuous in-depth study of image noise, image noise is classified, which is mainly composed of salt-and-pepper noise and Gaussian noise. Gray image is the simplest kind of image, and the brightness exists in the image in the form of two-dimensional distribution. The noise n
x
,
y of a gray-scale image can be regarded as the interference to image brightness. The description of noise is completed by the average value and variance of probability statistics [14].The average value of noise is generally used to describe the global intensity of noise, as shown in the following equation:(1)
n
¯
=
E
n
x
,
y
=
1
M
×
N
∑
x
=
1
M
∑
y
=
1
N
n
x
,
y
.The variance of noise is used to express the density of noise distribution on the image, as shown in the following equation:(2)
D
n
=
E
n
x
,
y
−
n
¯
2
=
1
M
×
N
∑
x
=
1
M
∑
y
=
1
N
n
x
,
y
−
n
¯
2
.


#### 3.1.1. Common Image Noise

According to probability, image noise can be divided into Gaussian noise and impulse noise. These noises are briefly introduced below.(1) Gaussian Noise. Gaussian noise is a common noise, and its mathematical expression is relatively simple, as shown in the following equation:
(3)
p
z
=
1
2
π
σ
exp
−
z
−
u
2
2
σ
2
.In Formula (3), z is the gray level of pixels, u is the mean value of z, σ is the standard deviation, and z exists in the form of normal distribution.(2) Impulse Noise (Salt-and-Pepper Noise). In image segmentation, the noise occurs frequently, which is specifically manifested as light noise in dark areas. The distribution function of dark noise in light areas can be given by the following equation:
(4)
p
z
=
P
a
,
z
=
a
,
P
b
,
z
=
b
,
0
,
other
.


#### 3.1.2. Common Image Denoising Algorithms

(1) Median Filter. This is an effective noise reduction method using sorting statistics. Its core algorithm is to calculate the average gray value of the neighborhood of a single point and characterize the gray value of a single point, and the isolated noise points are filtered out to filter out the interference of the pixels at this position. After Fourier change, the gray gradient of the high-frequency component is large. This method can smooth the image effect, but it can partially destroy the low-frequency component [15]. The mathematical expression of this method is shown in the following equation:
(5)
g
x
,
y
=
Med
f
x
−
k
,
y
−
l
,
k
,
l
∈
W
.f
x
,
y and g
x
,
y are the input and output, respectively. W is the neighborhood size of filtering, mostly 3
×
3 or 7
×
7 square, or linear or circular shaped which can be used as the neighborhood range of median filtering.By processing an image with salt-and-pepper noise, it can be found that the median filter has good filtering effect for the image with salt-and-pepper noise, with high restoration degree and very obvious image contour [16]. This is because the salt-and-pepper noises are discrete points. The average value of the gray value of the salt-and-pepper noise section is used to replace the original pixel value in order to filter the salt-and-pepper interference in the image. However, after filtering, the sharpness of the image decreases, and there will be slight sawtooth at the boundary.(2) Gaussian Filtering. Gaussian filtering belongs to the category of linear filtering, which can better filter the Gaussian noise in the image, and is widely used in the field of image denoising. The essence of Gaussian filtering is to remove noise through weighted average calculation. The pixel value of any point on the image is obtained by weighted averaging the initial value of the point and the values of other points in the neighborhood of the set size. Gaussian filtering can obtain filtering results in two ways: the first is Fourier transform and the second is convolution calculation with setting the neighborhood as the discretization window [17]. The second method is more common, but when setting a large neighborhood, the amount of calculation is very large. This is the first method that can be used. In Gaussian filtering, the weight of each pixel is determined by the adopted Gaussian function. The commonly used Gaussian function is shown in the following equation:
(6)
g
x
=
exp
−
x
2
2
σ
2
.In formula (6), σ is the mean square deviation, and the width of the Gaussian function changes with the size of this parameter.In the field of noise removal, Gaussian filtering often uses the discrete two-dimensional zero mean Gaussian function, as shown in the following equation:(7)
g
x
=
exp
−
x
2
+
y
2
2
σ
2
.The Gaussian filter performs the weighted average operation on the gray value of any point in the selected neighborhood and uses the operation result to replace the pixel value in the center of the neighborhood.


### 3.2. Improved Algorithm

The above algorithms are continuous algorithms based on the characteristics of target detection initialization. The tracking effect is not particularly ideal when the target characteristics change. This paper proposes an online learning target tracking algorithm. The biggest difference between it and the traditional tracking algorithm is the combination of detection algorithm and tracking algorithm. It uses the semisupervised learning mechanism to continuously update the tracking target. It has good tracking performance for target changes and partial occlusion, can adapt to more complex tracking environment, and has higher robustness [18]. The improved algorithm consists of three modules: detection, tracking, and learning, as shown in Figure 2.Figure 2
Improved mean shift composition framework.The tracking module is the basis of the whole system. It estimates the position of the target in the next frame image through the position of the target in the previous frame image. In the improved algorithm, the tracking module can enter the working mode only when the detection module determines that the target exists. During the working process of the tracking module, the tracking track generated by it is added to the positive sample training set of the learning module.The main function of the detection module is to estimate the error of the results of the tracking module. When the error is greater than the given threshold, intervene in the tracking results and correct the results. Its main working principle is to traverse the image pixels in the way of a sliding window, marking the positions similar to the target to be tracked as positive samples and the rest as negative samples. Pass the classification results to the learning module, and select a positive sample with the greatest confidence from the results as the initial position of the current tracking target to track the target in the next frame.The learning module mainly uses the positive and negative samples transmitted from the initial training set and the detection module to continuously improve the classifier through iterative methods to improve its classification accuracy.Before tracking, it is necessary to mark the position of the tracking target on the initial image manually, then input the position into the tracking module, track it through the mean hit tracking method, and transfer it to the learning module after obtaining the tracking results. In the learning module, the initialization target is selected as the positive sample input to the training set, and a part of the background area is selected as the negative sample training set to train the detection module. After such an initialization process, the detection module has the ability to judge, and then, the subsequent images can be inputted into the tracker. When the tracker obtains the detection results, it uses the detector to judge and correct, outputs the corrected results, and inputs the results into the learning module to improve the performance of the detector and ensure the accuracy of its detection. An online model will be established in the learning module to judge the results of the tracking module and determine that the target exists in the image. The model will also be updated online.In the improved algorithm, the tracking and detection results are transmitted to the learning module as samples, and the updated online model updates the tracking and detection module to ensure stable tracking when the tracking target is deformed [19].


### 3.3. Realize Sports Injury Posture Acquisition

This paper uses intelligent tracking to identify the athlete’s injury data. Firstly, it needs to confirm the injury mark of intelligent tracking. It is not possible to determine the extent of the injury because the expert’s guidance was used to determine the path to the above procedure. Assuming that the loss of intelligent trace image data is inn
a
,
t, its damage action is expressed by duplicate data c
+
y. The structure of the trauma matrix is shown in the following equation:
(8)
μ
c
y
=
∑
a
∑
t
a
−
a
¯
c
t
−
t
¯
y
n
a
,
t
,wherea and t represent the symbolic meaning of a sports injury and y
=
c
+
y
/
2
+
1. Through simultaneous normalization, equation (9) is obtained:
(9)
η
c
y
=
μ
c
y
μ
00
.Explain the time difference in the sports injury decision matrix, as shown in the following equation:(10)
ϕ
=
μ
c
y
+
η
c
y
.Because different intelligent tracking images have different shooting angles, it will affect the image definition. The state of the data is changed as shown in equation (11) to avoid image distortion and data loss:
(11)
W
c
y
=
μ
c
y
μ
c
2
−
μ
y
2
−
μ
μ
y
c
μ
c
−
μ
y
c
μ
y
,whereμ
c
2 is the athlete’s injury calibration data through the change of injury matrix and injury image display of intelligent tracking and μ
y
2 is the correlation parameter of damage. In order to realize the data positioning of the attitude, μ
y
2 is used as the judgment data, and μ
c and μ
y are the parameters of the motion attitude matrix.Damaged data can be obtained by determining the constraints on the model matrix and calculating the variance. If the difference is less than the required theoretical value, it can be used to subtract the theoretical value [20]. The implementation process of the motion injury attitude acquisition method based on intelligent tracking image analysis designed in this paper is shown in Figure 3.Figure 3
Implementation of motion injury posture acquisition method based on intelligent tracking image analysis.


## 3.1. Image Denoising

Noise refers to the factors that cause adverse effects in the processing or analysis of images. In most cases, the noise is a random signal and unpredictable, but it can be mathematically described by the method of probability and statistics. Noise comes from image acquisition, transmission, compression, and other links, in which the acquisition stage is the most serious link. When the noise is too large, it will affect the results of image processing and analysis and produce large errors [13]. Therefore, the first step of image analysis is to denoise the image, suppress the influence of noise, and improve the processing accuracy. With the continuous in-depth study of image noise, image noise is classified, which is mainly composed of salt-and-pepper noise and Gaussian noise. Gray image is the simplest kind of image, and the brightness exists in the image in the form of two-dimensional distribution. The noise n
x
,
y of a gray-scale image can be regarded as the interference to image brightness. The description of noise is completed by the average value and variance of probability statistics [14].The average value of noise is generally used to describe the global intensity of noise, as shown in the following equation:(1)
n
¯
=
E
n
x
,
y
=
1
M
×
N
∑
x
=
1
M
∑
y
=
1
N
n
x
,
y
.The variance of noise is used to express the density of noise distribution on the image, as shown in the following equation:(2)
D
n
=
E
n
x
,
y
−
n
¯
2
=
1
M
×
N
∑
x
=
1
M
∑
y
=
1
N
n
x
,
y
−
n
¯
2
.


### 3.1.1. Common Image Noise

According to probability, image noise can be divided into Gaussian noise and impulse noise. These noises are briefly introduced below.(1) Gaussian Noise. Gaussian noise is a common noise, and its mathematical expression is relatively simple, as shown in the following equation:
(3)
p
z
=
1
2
π
σ
exp
−
z
−
u
2
2
σ
2
.In Formula (3), z is the gray level of pixels, u is the mean value of z, σ is the standard deviation, and z exists in the form of normal distribution.(2) Impulse Noise (Salt-and-Pepper Noise). In image segmentation, the noise occurs frequently, which is specifically manifested as light noise in dark areas. The distribution function of dark noise in light areas can be given by the following equation:
(4)
p
z
=
P
a
,
z
=
a
,
P
b
,
z
=
b
,
0
,
other
.


### 3.1.2. Common Image Denoising Algorithms

(1) Median Filter. This is an effective noise reduction method using sorting statistics. Its core algorithm is to calculate the average gray value of the neighborhood of a single point and characterize the gray value of a single point, and the isolated noise points are filtered out to filter out the interference of the pixels at this position. After Fourier change, the gray gradient of the high-frequency component is large. This method can smooth the image effect, but it can partially destroy the low-frequency component [15]. The mathematical expression of this method is shown in the following equation:
(5)
g
x
,
y
=
Med
f
x
−
k
,
y
−
l
,
k
,
l
∈
W
.f
x
,
y and g
x
,
y are the input and output, respectively. W is the neighborhood size of filtering, mostly 3
×
3 or 7
×
7 square, or linear or circular shaped which can be used as the neighborhood range of median filtering.By processing an image with salt-and-pepper noise, it can be found that the median filter has good filtering effect for the image with salt-and-pepper noise, with high restoration degree and very obvious image contour [16]. This is because the salt-and-pepper noises are discrete points. The average value of the gray value of the salt-and-pepper noise section is used to replace the original pixel value in order to filter the salt-and-pepper interference in the image. However, after filtering, the sharpness of the image decreases, and there will be slight sawtooth at the boundary.(2) Gaussian Filtering. Gaussian filtering belongs to the category of linear filtering, which can better filter the Gaussian noise in the image, and is widely used in the field of image denoising. The essence of Gaussian filtering is to remove noise through weighted average calculation. The pixel value of any point on the image is obtained by weighted averaging the initial value of the point and the values of other points in the neighborhood of the set size. Gaussian filtering can obtain filtering results in two ways: the first is Fourier transform and the second is convolution calculation with setting the neighborhood as the discretization window [17]. The second method is more common, but when setting a large neighborhood, the amount of calculation is very large. This is the first method that can be used. In Gaussian filtering, the weight of each pixel is determined by the adopted Gaussian function. The commonly used Gaussian function is shown in the following equation:
(6)
g
x
=
exp
−
x
2
2
σ
2
.In formula (6), σ is the mean square deviation, and the width of the Gaussian function changes with the size of this parameter.In the field of noise removal, Gaussian filtering often uses the discrete two-dimensional zero mean Gaussian function, as shown in the following equation:(7)
g
x
=
exp
−
x
2
+
y
2
2
σ
2
.The Gaussian filter performs the weighted average operation on the gray value of any point in the selected neighborhood and uses the operation result to replace the pixel value in the center of the neighborhood.


## 3.1.1. Common Image Noise

According to probability, image noise can be divided into Gaussian noise and impulse noise. These noises are briefly introduced below.(1) Gaussian Noise. Gaussian noise is a common noise, and its mathematical expression is relatively simple, as shown in the following equation:
(3)
p
z
=
1
2
π
σ
exp
−
z
−
u
2
2
σ
2
.In Formula (3), z is the gray level of pixels, u is the mean value of z, σ is the standard deviation, and z exists in the form of normal distribution.(2) Impulse Noise (Salt-and-Pepper Noise). In image segmentation, the noise occurs frequently, which is specifically manifested as light noise in dark areas. The distribution function of dark noise in light areas can be given by the following equation:
(4)
p
z
=
P
a
,
z
=
a
,
P
b
,
z
=
b
,
0
,
other
.


## 3.1.2. Common Image Denoising Algorithms

(1) Median Filter. This is an effective noise reduction method using sorting statistics. Its core algorithm is to calculate the average gray value of the neighborhood of a single point and characterize the gray value of a single point, and the isolated noise points are filtered out to filter out the interference of the pixels at this position. After Fourier change, the gray gradient of the high-frequency component is large. This method can smooth the image effect, but it can partially destroy the low-frequency component [15]. The mathematical expression of this method is shown in the following equation:
(5)
g
x
,
y
=
Med
f
x
−
k
,
y
−
l
,
k
,
l
∈
W
.f
x
,
y and g
x
,
y are the input and output, respectively. W is the neighborhood size of filtering, mostly 3
×
3 or 7
×
7 square, or linear or circular shaped which can be used as the neighborhood range of median filtering.By processing an image with salt-and-pepper noise, it can be found that the median filter has good filtering effect for the image with salt-and-pepper noise, with high restoration degree and very obvious image contour [16]. This is because the salt-and-pepper noises are discrete points. The average value of the gray value of the salt-and-pepper noise section is used to replace the original pixel value in order to filter the salt-and-pepper interference in the image. However, after filtering, the sharpness of the image decreases, and there will be slight sawtooth at the boundary.(2) Gaussian Filtering. Gaussian filtering belongs to the category of linear filtering, which can better filter the Gaussian noise in the image, and is widely used in the field of image denoising. The essence of Gaussian filtering is to remove noise through weighted average calculation. The pixel value of any point on the image is obtained by weighted averaging the initial value of the point and the values of other points in the neighborhood of the set size. Gaussian filtering can obtain filtering results in two ways: the first is Fourier transform and the second is convolution calculation with setting the neighborhood as the discretization window [17]. The second method is more common, but when setting a large neighborhood, the amount of calculation is very large. This is the first method that can be used. In Gaussian filtering, the weight of each pixel is determined by the adopted Gaussian function. The commonly used Gaussian function is shown in the following equation:
(6)
g
x
=
exp
−
x
2
2
σ
2
.In formula (6), σ is the mean square deviation, and the width of the Gaussian function changes with the size of this parameter.In the field of noise removal, Gaussian filtering often uses the discrete two-dimensional zero mean Gaussian function, as shown in the following equation:(7)
g
x
=
exp
−
x
2
+
y
2
2
σ
2
.The Gaussian filter performs the weighted average operation on the gray value of any point in the selected neighborhood and uses the operation result to replace the pixel value in the center of the neighborhood.


## 3.2. Improved Algorithm

The above algorithms are continuous algorithms based on the characteristics of target detection initialization. The tracking effect is not particularly ideal when the target characteristics change. This paper proposes an online learning target tracking algorithm. The biggest difference between it and the traditional tracking algorithm is the combination of detection algorithm and tracking algorithm. It uses the semisupervised learning mechanism to continuously update the tracking target. It has good tracking performance for target changes and partial occlusion, can adapt to more complex tracking environment, and has higher robustness [18]. The improved algorithm consists of three modules: detection, tracking, and learning, as shown in Figure 2.Figure 2
Improved mean shift composition framework.The tracking module is the basis of the whole system. It estimates the position of the target in the next frame image through the position of the target in the previous frame image. In the improved algorithm, the tracking module can enter the working mode only when the detection module determines that the target exists. During the working process of the tracking module, the tracking track generated by it is added to the positive sample training set of the learning module.The main function of the detection module is to estimate the error of the results of the tracking module. When the error is greater than the given threshold, intervene in the tracking results and correct the results. Its main working principle is to traverse the image pixels in the way of a sliding window, marking the positions similar to the target to be tracked as positive samples and the rest as negative samples. Pass the classification results to the learning module, and select a positive sample with the greatest confidence from the results as the initial position of the current tracking target to track the target in the next frame.The learning module mainly uses the positive and negative samples transmitted from the initial training set and the detection module to continuously improve the classifier through iterative methods to improve its classification accuracy.Before tracking, it is necessary to mark the position of the tracking target on the initial image manually, then input the position into the tracking module, track it through the mean hit tracking method, and transfer it to the learning module after obtaining the tracking results. In the learning module, the initialization target is selected as the positive sample input to the training set, and a part of the background area is selected as the negative sample training set to train the detection module. After such an initialization process, the detection module has the ability to judge, and then, the subsequent images can be inputted into the tracker. When the tracker obtains the detection results, it uses the detector to judge and correct, outputs the corrected results, and inputs the results into the learning module to improve the performance of the detector and ensure the accuracy of its detection. An online model will be established in the learning module to judge the results of the tracking module and determine that the target exists in the image. The model will also be updated online.In the improved algorithm, the tracking and detection results are transmitted to the learning module as samples, and the updated online model updates the tracking and detection module to ensure stable tracking when the tracking target is deformed [19].


## 3.3. Realize Sports Injury Posture Acquisition

This paper uses intelligent tracking to identify the athlete’s injury data. Firstly, it needs to confirm the injury mark of intelligent tracking. It is not possible to determine the extent of the injury because the expert’s guidance was used to determine the path to the above procedure. Assuming that the loss of intelligent trace image data is inn
a
,
t, its damage action is expressed by duplicate data c
+
y. The structure of the trauma matrix is shown in the following equation:
(8)
μ
c
y
=
∑
a
∑
t
a
−
a
¯
c
t
−
t
¯
y
n
a
,
t
,wherea and t represent the symbolic meaning of a sports injury and y
=
c
+
y
/
2
+
1. Through simultaneous normalization, equation (9) is obtained:
(9)
η
c
y
=
μ
c
y
μ
00
.Explain the time difference in the sports injury decision matrix, as shown in the following equation:(10)
ϕ
=
μ
c
y
+
η
c
y
.Because different intelligent tracking images have different shooting angles, it will affect the image definition. The state of the data is changed as shown in equation (11) to avoid image distortion and data loss:
(11)
W
c
y
=
μ
c
y
μ
c
2
−
μ
y
2
−
μ
μ
y
c
μ
c
−
μ
y
c
μ
y
,whereμ
c
2 is the athlete’s injury calibration data through the change of injury matrix and injury image display of intelligent tracking and μ
y
2 is the correlation parameter of damage. In order to realize the data positioning of the attitude, μ
y
2 is used as the judgment data, and μ
c and μ
y are the parameters of the motion attitude matrix.Damaged data can be obtained by determining the constraints on the model matrix and calculating the variance. If the difference is less than the required theoretical value, it can be used to subtract the theoretical value [20]. The implementation process of the motion injury attitude acquisition method based on intelligent tracking image analysis designed in this paper is shown in Figure 3.Figure 3
Implementation of motion injury posture acquisition method based on intelligent tracking image analysis.


## 4. Results and Analysis

The software for the Arduino is designed to receive a DMP signal from an InvenSense MPU-6050 IMU to transmit a signal to a computer via an XBee-PRO wireless serial connection. The inserted DMP is in the IMU and can be used by the host processor to change the calculation of the operating algorithm. The DMP receives data from acceleration meters and gyroscopes and provides an integrated melting output. To display and retrieve data received from XBee, a computer program was developed that developed data-readable text using a communication port. The subjects were placed between four Kinect cameras, and the torso movement of the subjects was recorded with customized software [21]. Using the transformation matrix from the calibration step, stitching the images of each frame of the four cameras together can obtain a set of 3D point cloud information. By comparing the point clouds at each frame, the movement changes of the tested person can be accurately deduced. In order to deduce the changes of personnel actions, Geomagic Studio 2012 is used to calculate the set macrodata created. To sum up, the recording and deduction steps of the experimental data are as follows: load each point cloud exported from the customized software, build a 3D mesh, fill the mesh, and smooth the mesh with the mesh diagnosis tool. In order to ensure the accurate analysis of body motion, it is necessary to limit the analysis range, that is, place a boundary around the initial image and limit the volume calculation to the field of the human trunk. The database obtained by the experiment contains 2235 records of 144 different subjects, which perform a variety of complex actions. Since many records were sampled at 120 Hz while others were sampled at 60 Hz, the previous test sampling was reduced to 60 Hz. For evaluation, the preprocessed H3.6M data set is used to train the current model with 100 frames and 1660 ms time window. In this paper, the whole motion sequence is classified instead of a single motion sequence. The prediction ability of three models (S-TE, C-TE, and H-TE) is compared with the recently proposed ERD classification prediction algorithm. These models have been trained by recording the H3.6M data set. Reduce the sampling frequency to 25 Hz and convert the joint angle into an exponential graph. When the time window covers about 1660 Ms, the cyclic network will be initialized to 40 frames, equivalent to 1600 Ms. For each action, a separate pretraining recursive model is used. Although LSTM3l performs better than some models in the initial prediction, the time encoder C-TE shows better performance in the prediction of 160 ms or more. Because human action is a complex nonstationary action, it is difficult for the cyclic network to make a short-term prediction, but the model can infer the future prediction framework. In most predictions, the symmetric time encoder S-TE and convolutional time encoder C-TE are better than the layered time encoder H-TE, which shows that the structure a priori is beneficial to motion prediction. The hybrid coding method reduces the prediction error of fine-tuning specific actions and can effectively classify and predict the actions not included in the original training data. Movement processes in a simulation environment are obtained using computer simulations that mimic the damage of various processes. The measurement criteria are shown in Table 1.Table 1
Simulation parameter setting.
Simulation parameters
Set value
Transportation damage degree
Below level 3 damage
Relative muscle exercise
More than 40%
Simulation image floating parameters
[-10, 50]
Data fixed point quantity (GB)
6
Exercise interval (s)
[8, 15]
Simulation times
760
Simulation duration (min)
20Modeling a smart tracking image detection method based on the direction of the environment penetrates a potentially harmful operating system. Taking advantage of sports performance, a moving environment is created and image control skills detect discrepancies. Simulation of posture judgment of potential sports injuries is based on intelligent tracking image analysis.Destruction of existing and new processes is done on various examples in the sports field, as shown in Figure4.Figure 4
Damage detection speed of traditional method and new method.Thex-axis in Figure 4 represents the time in minutes. The y-axis represents the speed of the sports injury. In different perspectives, traditional detection speeds are slower and less efficient. The actual damage to the traditional and new process sequences in the game environment was examined using multiple point examples, as shown in Figure 5.Figure 5
Accuracy of damage judgment of traditional method and new method.In Figure5, the x-axis represents the sequence of magnitudes. The y-axis represents the accuracy of sports decisions. Many finds are similar, but the results are often less accurate.In an athletics environment, the degree of injury to the athlete shall be checked with normal and new techniques as shown in Figure6.Figure 6
Sports injury degree of traditional method and new method.In Figure6, the x-axis represents time. The y-axis indicates the degree of physical injury. The simulation time is the same, but the damage in technical sports is always high. From the simulation results, the modeling of physical fitness based on triangular analysis improves the incompleteness of the initial fitness information, which is limited only by the characteristics that can prevent injury according to the instructions. With the participation of experts, the continuity of movement from the site of injury will be improved, the evolution of injuries will be more clearly understood, and the average of athletes will be correctly distributed [22]. It reduces the risk of injury to athletes, improves the speed and accuracy of injury decisions, reduces the incidence of injuries to athletes, and improves performance. This reduces the incidence of blindness in athletes and prevents waste of resources.


## 5. Conclusion

This paper provides a way to get an injury of movement based on intelligent image analysis skills. The introduction of intelligent imaging analysis techniques to monitor the location of sports injuries, the use of intelligent simulation control techniques to count the procedures to correct movement restrictions, and the use of counting procedures instead of the traditional selection process can be avoided to avoid multiple procedures to change the quantity and finally understand the collection information at the site of the sports injury. At this stage, the analysis and research to determine the location of potential sports injuries revealed that there are insufficient rules for locating sports injuries in a self-limiting environment to prevent sports injuries. We offer an intelligent control imaging analysis based on a model that can be used to make decisions about physical harm. Using the calculation process instead of the traditional selection process can effectively avoid the selection of process quantity and variation quantity and finally realize the information acquisition of motion injury posture. The simulations try to prove the accuracy of the received, to adopt a method of observing sharp images in moving space, and to solve the problem of injuries of existing athletes. It is impossible to make a correct and timely conclusion. This meets the unsustainable need for intelligent tracking images found in heavy work environments. It can accurately and quickly record tracking images of smart athletes to identify the body of an injured athlete. It effectively improves the ability to prevent sports injuries and ensures the safety and health of athletes.



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*Source: 5192601-2022-08-31.xml*