# Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm

Authors: Wujun Xiang
Journal: Mobile Information Systems
 (2022)

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## Abstract

The proportion of network video traffic in the network is increasing as Internet and streaming media technology continues to advance. The current network environment is becoming more complicated as new application services emerge. ISPs are grappling with how to effectively allocate limited network bandwidth resources while maintaining network service quality (Internet service provider). Currently, in the process of physical coaching, trainers use the video replay and analysis management method with less difficulty to explain the essentials of movements to athletes, but it is not intuitive enough to identify and classify network video services and can guide Internet service providers (ISPs) to optimise network resource management and rationally configure traffic engineering, in order to ensure the quality of service (QOS) of video services. Finally, using the shot as the unit, find similar video clips in the video database based on the video examples submitted by the user, and return retrieval results based on similarity. This paper uses streaming video as the research object and examines the transmission control of sports video using the particle swarm optimization algorithm in detail, based on a comprehensive introduction to the analysis and recognition of sports video in the process of physical coaching.


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## Body

## 1. Introduction

In today’s world, the Internet has become fully integrated into our daily lives as a result of continuous advancements in Internet technology, and it is profoundly changing global interest patterns, economic patterns, and security patterns [1]. In society, the Internet has gradually formed a new economic form. “Intelligence” and “integration” have become the main core features of Internet development in the new era [2] as a result of “internet plus.” The global Internet is entering a new era of intelligent convergence, and smart homes, car networking, virtual reality, and other industries have emerged as competitive hotspots, furthering societal development, and drastically altering people’s lifestyles [3]. Video data, as an expression medium for information, not only contains low-level semantics like colour, texture, object motion, and spatial position information between objects, but it also contains high-level abstract semantics of events [4]. For example, we can learn the character’s emotion, the event’s background, the causal relationship of the event, and so on from video data [5]. Simultaneously, Internet content has evolved from traditional simple text information to multimedia information [6], with the proportion of video data increasing day by day, and the advent of streaming media technology provides a strong technical guarantee for the application and popularisation of network video transmission [7].Traditional keyword-based text data retrieval methods rely on humans to manually annotate multimedia clips like videos. When querying, the content of videos is ignored, and retrieval is based solely on precalibrated tags, such as video keywords [8, 9]. The particle swarm algorithm-based video analysis and recognition of physical coaching can effectively analyse information by collecting a large number of video images of high-level athletes in normal training and competition, and analyse the athletes’ movement trajectory by tracking the athletes in real time, which can change the training mode in which coaches guide athletes’ technical movements only by manual observation and experience and improve the standardisation. Traditional network applications such as FTP, Email, and Web applications are accounting for less and less network traffic, while emerging applications such as live webcasts, online games, streaming media videos, and instant messaging applications are accounting for more and more [10]. We can effectively reduce packet delay and packet loss rate, avoid network congestion to some extent, and ensure that the multimedia receiver can receive as many data packets as possible within the effective time by controlling the transmission of streaming media through feedback control and some optimization control methods [11], to ensure smooth video and audio playback and the desired picture and sound effect [12]. How to improve network service quality with limited network resources while ensuring user QoE (quality of experience) has become an urgent problem for ISPs (Internet service providers) to solve [13]. At the same time, the current Internet architecture and best-effort service mode are insufficient for streaming media transmission [14].As a result, implementing particle swarm optimization to analyse and recognise sports video in the physical coaching process is critical in order to guide Internet service providers (ISPs) in better allocating bandwidth resources, providing better services for users, and ensuring a positive user experience. It can also ensure that athletes intuitively understand their own shortcomings and improve the quality of physical training under the guidance of coaches during the physical training process [15].


## 2. Related Work

To effectively distinguish P2P traffic, literature [16] proposed a semisupervised clustering algorithm that used four statistical characteristics of packet size, maximum, minimum, mean, and standard deviation. A traffic classification method based on multiobjective evolutionary fuzzy classifiers was proposed in the literature [17] (MOE FCS). MOEFCs have a high level of equilibrium precision and interpretability. The advantages and disadvantages of existing identification and classification methods in identifying various P2P services are discussed in literature [18]. Then, based on support vector machine research, a new P2P traffic identification model is proposed, which uses three statistical features to effectively detect P2P video analysis. An improved method of random forest network flow classification was proposed in the literature [19]. For the first time, literature [20] classified text and image information from WeChat and discovered C4. The decision tree algorithm has the best performance, with an overall accuracy of 99.93 percent. Based on feature weight learning, reference [21] proposed an improved support vector machine network traffic classification method. Feature weight learning is used in this method, and the corresponding weight is assigned based on the importance of each feature to classification. C4 5 and random forest are compared in detail in reference [22], and it is noted that C4 5 has the highest recognition accuracy, and random forest has the highest operation efficiency in the experimental data set of 25000 samples. A method for automatically extracting the field colour in sports video was proposed in the literature [23]. Unlike previous studies, this method obtains the parameters using a Gaussian mixture model and algorithm. The experiment is similar to the one that came before it. The hidden Naive Bayes (HNB) model is used to classify video analysis in the literature [24], and the classification accuracy is 15% higher than that of tree augmented Naive Bayes (tan). A one-dimensional convolutional neural network-based end-to-end encrypted traffic classification method was proposed in the literature [25]. This method combines feature extraction, feature selection, and a classifier into a single end-to-end framework with the goal of learning the nonlinear relationship between the original input and expected output automatically.


## 3. Analysis and Recognition of Sports Video in Sports Training

### 3.1. Adaptive Threshold Moving Target Separation Based on Particle Filter Prediction

How to select appropriate feature subsets from massive data has become another research hotspot in video analysis and classification [26]. The schematic diagram of particle filter prediction is shown in Figure 1 below.Figure 1
Schematic diagram of particle filter prediction.Source IP address, source port number, destination IP address, destination port number, and transport layer protocol type are among the features. For example, the quintuple 192.168.168.1, 100, 192.168.168.2, 80, and TCP means that a terminal with an IP address of 192.168.168.1 passes port 100, uses the TCP protocol, and has an IP address of 192.168.168.1. The connection weights of neural networks are usually determined in one of two ways: one, directly according to specific requirements, such as when the Hopfield network is used for optimization calculation, and the other, through learning, which is used by most artificial neural networks [27–29]. It is a new multimedia information processing and organisation technology that is not like the old retrieval methods. Content-based video retrieval differs from traditional keyword-based text retrieval due to the differences between video and traditional text data. Each colour space has its own context or application field, and different colour spaces should be used for different situations [30].The athletes in the training video sequence separation are the moving target. To improve the accuracy of moving target collection, an adaptive threshold moving target separation algorithm based on particle filter prediction is used. The faster the speed, the better in practise. Unless there is an oversized storage device, the faster the speed is at the expense of the pixels in each image frame. Moving object separation is a crucial preprocessing step for video analysis and classification using machine learning methods, and it has a big impact on the classifier’s performance. For data transmission, most network applications will use the TCP or UDP protocols. The type of application will be indicated by a 16-bit identifier in the corresponding packet header. The application’s port number is this identifier. The neural network automatically corrects the weight of the interconnections in the network and gradually reduces the error between the actual output and the target output using a learning algorithm. The neural network automatically corrects the weight of the interconnections in the network and gradually reduces the error between the actual output and the target output using a learning algorithm. Analyzing color images directly with hue and saturation yields better results that reflect the nature of the color. Extraction of keyframes: a key step in video processing is shot-segmentation-based moving object separation, which involves extracting one or more image frames that can reflect the main content of a video shot. The quality of keyframes has a significant impact on content-based video retrieval performance.


### 3.2. Moving Object Tracking in Moving Video

The training process starts to learn according to the successfully separated basic action video sequence and motion model, and the conditional random field model of athletes’ basic actions is obtained through training. The recognition process is shown in Figure2.Figure 2
Identification process.Tracking is mainly composed of two parts: prime search algorithm and feature evaluation. By using the search algorithm, we can find the subset that gives the best score of the feature evaluation function, that is, the optimal subset. The principle of the flow classification method based on port number mapping is to realize traffic identification according to the one-to-one mapping relationship between the port number allocated by the Internet assigned numbers authority (IANA) and the network application. Although the operation function of each neuron is very simple and the signal transmission rate is low (about 110 times/s), due to the extremely parallel interconnection function between neurons, an ordinary human brain can finally complete the task that the current computer needs at least 1.1 billion processing steps in about 1 second. In the actual process of video retrieval service, the ultimate requirement of users is to be able to directly access the specified objects in the video, such as whether a star has played a certain role in a movie. Based on probabilistic reasoning and sampling process, the random search method analyzes the influence of features on classification results, gives different weights to each feature in the search process, and finally selects the feature subset according to the set threshold. The method is to detect the payload of the data packet by DPI (deep packet inspection) technology, then identify the unique protocol string or signature that can be distinguished from different applications in the payload content and then find out whether the string or signature is contained in the feature library, so as to achieve the purpose of network flow identification and classification. Because the pixel comparison method is very sensitive to the motion of objects, many researchers propose to use the color histogram method to detect the lens boundary. If the number of clusters is too small, the classification results will be inaccurate, resulting in many backgrounds being divided into foreground targets. If the number of clusters is too large, it will be seriously interfered by noise and other factors, and it will not be easy to see the change of the rule of cluster center value, and the cost of the algorithm will also increase.Each feature subset generation is random and has no bearing on the current feature subset. The size of the search space can be limited by changing the maximum number of iterations, despite the fact that the search space is O(2N). The elements of the activation mode (input vector) are provided by the source node of the input layer to form the input signal of the neuron (calculation node) of the second layer (the first hidden layer). The second layer’s output signal is used as the input for the third layer, which is constantly transmitted. The network output is given by the final output layer in relation to the source node’s activation mode. It divides the image’s grey space into components and then counts how many pixels fall into each subspace. This method overcomes the drawback of relying too heavily on port numbers, and it is simple, practical, and accurate. However, this method requires constant updating of the feature library, a large amount of data, and a large amount of processing and storage resources. Furthermore, for unknown application types or encrypted data traffic, this method is useless.


## 3.1. Adaptive Threshold Moving Target Separation Based on Particle Filter Prediction

How to select appropriate feature subsets from massive data has become another research hotspot in video analysis and classification [26]. The schematic diagram of particle filter prediction is shown in Figure 1 below.Figure 1
Schematic diagram of particle filter prediction.Source IP address, source port number, destination IP address, destination port number, and transport layer protocol type are among the features. For example, the quintuple 192.168.168.1, 100, 192.168.168.2, 80, and TCP means that a terminal with an IP address of 192.168.168.1 passes port 100, uses the TCP protocol, and has an IP address of 192.168.168.1. The connection weights of neural networks are usually determined in one of two ways: one, directly according to specific requirements, such as when the Hopfield network is used for optimization calculation, and the other, through learning, which is used by most artificial neural networks [27–29]. It is a new multimedia information processing and organisation technology that is not like the old retrieval methods. Content-based video retrieval differs from traditional keyword-based text retrieval due to the differences between video and traditional text data. Each colour space has its own context or application field, and different colour spaces should be used for different situations [30].The athletes in the training video sequence separation are the moving target. To improve the accuracy of moving target collection, an adaptive threshold moving target separation algorithm based on particle filter prediction is used. The faster the speed, the better in practise. Unless there is an oversized storage device, the faster the speed is at the expense of the pixels in each image frame. Moving object separation is a crucial preprocessing step for video analysis and classification using machine learning methods, and it has a big impact on the classifier’s performance. For data transmission, most network applications will use the TCP or UDP protocols. The type of application will be indicated by a 16-bit identifier in the corresponding packet header. The application’s port number is this identifier. The neural network automatically corrects the weight of the interconnections in the network and gradually reduces the error between the actual output and the target output using a learning algorithm. The neural network automatically corrects the weight of the interconnections in the network and gradually reduces the error between the actual output and the target output using a learning algorithm. Analyzing color images directly with hue and saturation yields better results that reflect the nature of the color. Extraction of keyframes: a key step in video processing is shot-segmentation-based moving object separation, which involves extracting one or more image frames that can reflect the main content of a video shot. The quality of keyframes has a significant impact on content-based video retrieval performance.


## 3.2. Moving Object Tracking in Moving Video

The training process starts to learn according to the successfully separated basic action video sequence and motion model, and the conditional random field model of athletes’ basic actions is obtained through training. The recognition process is shown in Figure2.Figure 2
Identification process.Tracking is mainly composed of two parts: prime search algorithm and feature evaluation. By using the search algorithm, we can find the subset that gives the best score of the feature evaluation function, that is, the optimal subset. The principle of the flow classification method based on port number mapping is to realize traffic identification according to the one-to-one mapping relationship between the port number allocated by the Internet assigned numbers authority (IANA) and the network application. Although the operation function of each neuron is very simple and the signal transmission rate is low (about 110 times/s), due to the extremely parallel interconnection function between neurons, an ordinary human brain can finally complete the task that the current computer needs at least 1.1 billion processing steps in about 1 second. In the actual process of video retrieval service, the ultimate requirement of users is to be able to directly access the specified objects in the video, such as whether a star has played a certain role in a movie. Based on probabilistic reasoning and sampling process, the random search method analyzes the influence of features on classification results, gives different weights to each feature in the search process, and finally selects the feature subset according to the set threshold. The method is to detect the payload of the data packet by DPI (deep packet inspection) technology, then identify the unique protocol string or signature that can be distinguished from different applications in the payload content and then find out whether the string or signature is contained in the feature library, so as to achieve the purpose of network flow identification and classification. Because the pixel comparison method is very sensitive to the motion of objects, many researchers propose to use the color histogram method to detect the lens boundary. If the number of clusters is too small, the classification results will be inaccurate, resulting in many backgrounds being divided into foreground targets. If the number of clusters is too large, it will be seriously interfered by noise and other factors, and it will not be easy to see the change of the rule of cluster center value, and the cost of the algorithm will also increase.Each feature subset generation is random and has no bearing on the current feature subset. The size of the search space can be limited by changing the maximum number of iterations, despite the fact that the search space is O(2N). The elements of the activation mode (input vector) are provided by the source node of the input layer to form the input signal of the neuron (calculation node) of the second layer (the first hidden layer). The second layer’s output signal is used as the input for the third layer, which is constantly transmitted. The network output is given by the final output layer in relation to the source node’s activation mode. It divides the image’s grey space into components and then counts how many pixels fall into each subspace. This method overcomes the drawback of relying too heavily on port numbers, and it is simple, practical, and accurate. However, this method requires constant updating of the feature library, a large amount of data, and a large amount of processing and storage resources. Furthermore, for unknown application types or encrypted data traffic, this method is useless.


## 4. Analysis of Sports Video Transmission Control Based on Particle Swarm Optimization Algorithm

### 4.1. Motion Feature Extraction

The purpose of key frame extraction is to eliminate the duplicate information of images in the shot and to represent the main content of the shot with one or more images that can express the content of the shot, thus reducing redundancy and facilitating the management of content-based video retrieval process. The goal of sports video recognition in physical coaching is to identify irregular training actions and then to constrain the training standards adopted by athletes. The mean filtering method can restrain the bad invasion of noise, and the new background separation probability obtained is as follows:(1)Pfi,j=19∑m=−11∑n=−11Pi+m,j+n.For feature selection, relevance measurement mainly measures the importance of features from two aspects: one is to judge whether there is redundancy between features according to the correlation between features; the other is to judge the prediction ability of features according to the correlation between features and categories. Due to the interference of the parameter estimation deviation in the camera static motion model on the post-processing results, the false separation rate of the traditional Chinese method increases significantly in the later stage, but the false separation rate of this method does not change significantly, which shows that this method can realize the accurate separation of sports video, as shown in Figure3.Figure 3
Effect of different methods on false separation rate of each frame in training.For category A, recall represents the proportion of the number of samples correctly classified as category A to the total number of samples in category A; the precision rate represents the proportion of the number of samples correctly classified as category A to the number of samples classified as category A. In practical applications, there are often multiple access terminals. The sports trend and times in the process of physical coaching are regular. Therefore, the second-order autoregressive sports model for physical coaching is:(2)Xt−Xt−1=Xt−1Xt−2+ηWt.Because there are multiple access terminals, if an output link is configured for each access terminal, it will not only waste resources but also have low utilization rate of the link. In order to meet the actual requirements such as improving the utilization rate of the link, the data streams of each access terminal will be multiplexed and then transmitted uniformly through a multiplexer. Firstly, it is necessary to initialize the particles, manually identify the important joint points of the skeleton model corresponding to the first frame and the second frame of video tracking, initialize the particles of different joint points and then shape the motion model that can be matched based on the positions of each joint point, and predict the positions and weights of particles at different important joint points in the subsequent motion video frames according to the motion model. If the weight of particles is higher than the set threshold, it means that the motion model is inaccurate and should be improved in time.Y-axis motion coordinate system is the coordinate system set for athletes’ joints, and the center of joints is the origin. The calculation method of each joint displacement is as follows:
(3)Δx=x2−x1.For example, for a stationary lens, there is no form of motion. Even if the duration of the lens is very long, it is not necessary to extract too many key frames. The accuracy andF-measure of the RFPOS feature selection algorithm proposed in this paper are higher than those of the Relief-PSO algorithm on UCI data sets, and the accuracy can reach more than 93% on wine and ionosphere data sets. The comparison results are shown in Figure 4 below.Figure 4
The algorithm in this paper is compared with the Relief-PSO algorithm.The tone probability distribution projection image generated by histogram back projection, that is, the back projection image of histogram. Each pixel value in the probability image represents the probability that the input pixel belongs to the histogram box. Compare the actual action of athletes with the standard action in the database, extract the difference between the values, and then, get the semantic rules through mapping. Replace the semantic rules entered in advance with the action realization state represented by concise data, the actions that need to be improved and how to improve them. The feature subset obtained by using correlation measurement maintains a high correlation with the category and a low correlation with other features. At the same time, in order to study the operation efficiency of the algorithm, two feature selection methods are used to conduct 30 repeated feature selection experiments on the same UCI data set. The average time overhead of the two feature selection methods is shown in Figure5.Figure 5
Average time cost of two feature selection methods.Among them, TP (true positive) is called true, which means that the actual and classified results are positive; FP (false positive) is called false positive, which means that it is actually a negative class and is classified as a positive class. TN (true negative) is called true negative, which means that both the actual and classified results are negative classes. FN (false negative) is called false negative, which means that it is actually a positive class and is classified as a negative class. The token bucket mechanism is widely used in streaming media video transmission control because it is simple, effective, and easy to implement. At the same time, it is a critical component of this type of transmission control scheme, as the token generation rate is the key to influencing the overall control effect. To control the access of multiple access terminals, multiple token buckets are used. Each access terminal can be a streaming media server or any other data stream source for streaming media. The farther away from the centre of the athlete area, the less reliable the pixel is, and the more likely it is covered by other objects or belongs to the background.


### 4.2. Global Motion Feature Extraction

Global motion and local motion. Global motion is mainly caused by camera motion, such as camera translation, rotation, and stretching. Therefore, it is reasonable to give different weights to the pixels at different positions in the athlete area when calculating the histogram of the athlete area. The fluctuation range of the downstream packet size of live video is larger, but it is mainly between 400 and 600 bytes. The camera static motion model between two frames can be expressed as:(4)x=ax′+by′+e,y=cx′+dy′+f.The data packets of HTTP video download service are generally maintained at a high level, ranging from 1430 to 1520 bytes. The comparison of the downlink average packet sizes of the three network video services is shown in Figure6 below.Figure 6
Comparison of downlink average packet size of three network video services.The reference coordinate system is established by using the stationary coordinate system and the motion coordinate system, in which theZ-axis faces the paper according to the right-hand coordinate system, and the origin of the X-axis stationary coordinate system is the J1 joint point of the athletes in the original state in Hanim specification. If the distance between categories is larger, the separability between categories is larger, and the accuracy of classification is higher. However, for the nonlinear classification problem, we need to transform the nonlinear problem into a linear problem through a nonlinear transformation. This is because as u increases, the number of valid tokens in the token bucket increases. Then, for the data in the token bucket buffer, there are more opportunities to flow into the multiplexer, so the delay and packet loss are reduced. Therefore, the fuzzy function sigmoid:
(5)sigvidn+1=11+exp−vidn+1.For example, the video of a person standing for a long time in a still background has a small amount of motion and simple form of motion. The fluctuation of Kan video is the most irregular, because data transmission is affected by shared nodes, and the average packet arrival time interval of two stream sequence numbers is shown in Figure7 below.Figure 7
Average packet arrival time interval of two flow sequence numbers.The sports forms include camera translation, stretching, and character sports. In the process of tracking athletes with the algorithm, a rectangular box is used to represent the tracked athletes. In most cases, the box contains background pixels or pixels of adjacent objects. Therefore, the histogram generated by the color information in the rectangular box also contains the characteristics of interfering objects or background. The acquired video of the moving target and the standard moving target are synthesized together, so that the standard moving target and the athlete’s moving target can be compared in an overlapping way, and the two moving targets can also be played synchronously on the same screen, so that the movement differences between the two movements can be accurately determined, and the technical movements of different moving targets can be visually and clearly compared. Take a scene in the video, and the frame rate at different times is shown in Figure8 below.Figure 8
Frame rate.By detecting the input motion video separation sequenceX, the parameters of conditional random field model can be obtained, and the class with higher parameter value belongs to the corresponding class. When the sample sets are nonlinear, the original feature space can be mapped to the higher-dimensional target space by nonlinear transformation, thus transforming the nonlinear problem into a linear problem in the high-dimensional space. In order to avoid the “dimension disaster” and the high computational complexity of high-dimensional space, SVM uses kernel function to indirectly realize the mapping from low-dimensional space to high-dimensional space. The SVM kernel function expression is as follows:
(6)fitness=NinP.Due to the limited output capacity of the multiplexer at this time, whenC is less than the average rate of the input stream, the rate of token generation is relatively low. To prevent a large amount of data loss in the multiplexer, the size of the data stream flowing into the multiplexer is limited. Because a large amount of input data is cached in the token bucket’s buffer, the token bucket currently has a token shortage. The fitness function calculates the fitness of the objective vector and each particle, with the particle with the highest fitness being selected as the particle swarm optimization solution. Calculate the optimal solution vector’s interval. Because of the algorithm’s complexity, calculating the optical flow of the entire image takes a long time, and most videos have less local motion in the upper left corner. As a result, to represent the overall motion of the image, only the optical flow of the region is extracted.


## 4.1. Motion Feature Extraction

The purpose of key frame extraction is to eliminate the duplicate information of images in the shot and to represent the main content of the shot with one or more images that can express the content of the shot, thus reducing redundancy and facilitating the management of content-based video retrieval process. The goal of sports video recognition in physical coaching is to identify irregular training actions and then to constrain the training standards adopted by athletes. The mean filtering method can restrain the bad invasion of noise, and the new background separation probability obtained is as follows:(1)Pfi,j=19∑m=−11∑n=−11Pi+m,j+n.For feature selection, relevance measurement mainly measures the importance of features from two aspects: one is to judge whether there is redundancy between features according to the correlation between features; the other is to judge the prediction ability of features according to the correlation between features and categories. Due to the interference of the parameter estimation deviation in the camera static motion model on the post-processing results, the false separation rate of the traditional Chinese method increases significantly in the later stage, but the false separation rate of this method does not change significantly, which shows that this method can realize the accurate separation of sports video, as shown in Figure3.Figure 3
Effect of different methods on false separation rate of each frame in training.For category A, recall represents the proportion of the number of samples correctly classified as category A to the total number of samples in category A; the precision rate represents the proportion of the number of samples correctly classified as category A to the number of samples classified as category A. In practical applications, there are often multiple access terminals. The sports trend and times in the process of physical coaching are regular. Therefore, the second-order autoregressive sports model for physical coaching is:(2)Xt−Xt−1=Xt−1Xt−2+ηWt.Because there are multiple access terminals, if an output link is configured for each access terminal, it will not only waste resources but also have low utilization rate of the link. In order to meet the actual requirements such as improving the utilization rate of the link, the data streams of each access terminal will be multiplexed and then transmitted uniformly through a multiplexer. Firstly, it is necessary to initialize the particles, manually identify the important joint points of the skeleton model corresponding to the first frame and the second frame of video tracking, initialize the particles of different joint points and then shape the motion model that can be matched based on the positions of each joint point, and predict the positions and weights of particles at different important joint points in the subsequent motion video frames according to the motion model. If the weight of particles is higher than the set threshold, it means that the motion model is inaccurate and should be improved in time.Y-axis motion coordinate system is the coordinate system set for athletes’ joints, and the center of joints is the origin. The calculation method of each joint displacement is as follows:
(3)Δx=x2−x1.For example, for a stationary lens, there is no form of motion. Even if the duration of the lens is very long, it is not necessary to extract too many key frames. The accuracy andF-measure of the RFPOS feature selection algorithm proposed in this paper are higher than those of the Relief-PSO algorithm on UCI data sets, and the accuracy can reach more than 93% on wine and ionosphere data sets. The comparison results are shown in Figure 4 below.Figure 4
The algorithm in this paper is compared with the Relief-PSO algorithm.The tone probability distribution projection image generated by histogram back projection, that is, the back projection image of histogram. Each pixel value in the probability image represents the probability that the input pixel belongs to the histogram box. Compare the actual action of athletes with the standard action in the database, extract the difference between the values, and then, get the semantic rules through mapping. Replace the semantic rules entered in advance with the action realization state represented by concise data, the actions that need to be improved and how to improve them. The feature subset obtained by using correlation measurement maintains a high correlation with the category and a low correlation with other features. At the same time, in order to study the operation efficiency of the algorithm, two feature selection methods are used to conduct 30 repeated feature selection experiments on the same UCI data set. The average time overhead of the two feature selection methods is shown in Figure5.Figure 5
Average time cost of two feature selection methods.Among them, TP (true positive) is called true, which means that the actual and classified results are positive; FP (false positive) is called false positive, which means that it is actually a negative class and is classified as a positive class. TN (true negative) is called true negative, which means that both the actual and classified results are negative classes. FN (false negative) is called false negative, which means that it is actually a positive class and is classified as a negative class. The token bucket mechanism is widely used in streaming media video transmission control because it is simple, effective, and easy to implement. At the same time, it is a critical component of this type of transmission control scheme, as the token generation rate is the key to influencing the overall control effect. To control the access of multiple access terminals, multiple token buckets are used. Each access terminal can be a streaming media server or any other data stream source for streaming media. The farther away from the centre of the athlete area, the less reliable the pixel is, and the more likely it is covered by other objects or belongs to the background.


## 4.2. Global Motion Feature Extraction

Global motion and local motion. Global motion is mainly caused by camera motion, such as camera translation, rotation, and stretching. Therefore, it is reasonable to give different weights to the pixels at different positions in the athlete area when calculating the histogram of the athlete area. The fluctuation range of the downstream packet size of live video is larger, but it is mainly between 400 and 600 bytes. The camera static motion model between two frames can be expressed as:(4)x=ax′+by′+e,y=cx′+dy′+f.The data packets of HTTP video download service are generally maintained at a high level, ranging from 1430 to 1520 bytes. The comparison of the downlink average packet sizes of the three network video services is shown in Figure6 below.Figure 6
Comparison of downlink average packet size of three network video services.The reference coordinate system is established by using the stationary coordinate system and the motion coordinate system, in which theZ-axis faces the paper according to the right-hand coordinate system, and the origin of the X-axis stationary coordinate system is the J1 joint point of the athletes in the original state in Hanim specification. If the distance between categories is larger, the separability between categories is larger, and the accuracy of classification is higher. However, for the nonlinear classification problem, we need to transform the nonlinear problem into a linear problem through a nonlinear transformation. This is because as u increases, the number of valid tokens in the token bucket increases. Then, for the data in the token bucket buffer, there are more opportunities to flow into the multiplexer, so the delay and packet loss are reduced. Therefore, the fuzzy function sigmoid:
(5)sigvidn+1=11+exp−vidn+1.For example, the video of a person standing for a long time in a still background has a small amount of motion and simple form of motion. The fluctuation of Kan video is the most irregular, because data transmission is affected by shared nodes, and the average packet arrival time interval of two stream sequence numbers is shown in Figure7 below.Figure 7
Average packet arrival time interval of two flow sequence numbers.The sports forms include camera translation, stretching, and character sports. In the process of tracking athletes with the algorithm, a rectangular box is used to represent the tracked athletes. In most cases, the box contains background pixels or pixels of adjacent objects. Therefore, the histogram generated by the color information in the rectangular box also contains the characteristics of interfering objects or background. The acquired video of the moving target and the standard moving target are synthesized together, so that the standard moving target and the athlete’s moving target can be compared in an overlapping way, and the two moving targets can also be played synchronously on the same screen, so that the movement differences between the two movements can be accurately determined, and the technical movements of different moving targets can be visually and clearly compared. Take a scene in the video, and the frame rate at different times is shown in Figure8 below.Figure 8
Frame rate.By detecting the input motion video separation sequenceX, the parameters of conditional random field model can be obtained, and the class with higher parameter value belongs to the corresponding class. When the sample sets are nonlinear, the original feature space can be mapped to the higher-dimensional target space by nonlinear transformation, thus transforming the nonlinear problem into a linear problem in the high-dimensional space. In order to avoid the “dimension disaster” and the high computational complexity of high-dimensional space, SVM uses kernel function to indirectly realize the mapping from low-dimensional space to high-dimensional space. The SVM kernel function expression is as follows:
(6)fitness=NinP.Due to the limited output capacity of the multiplexer at this time, whenC is less than the average rate of the input stream, the rate of token generation is relatively low. To prevent a large amount of data loss in the multiplexer, the size of the data stream flowing into the multiplexer is limited. Because a large amount of input data is cached in the token bucket’s buffer, the token bucket currently has a token shortage. The fitness function calculates the fitness of the objective vector and each particle, with the particle with the highest fitness being selected as the particle swarm optimization solution. Calculate the optimal solution vector’s interval. Because of the algorithm’s complexity, calculating the optical flow of the entire image takes a long time, and most videos have less local motion in the upper left corner. As a result, to represent the overall motion of the image, only the optical flow of the region is extracted.


## 5. Conclusion

Various emerging application types have continuously emerged in recent years, causing network data flow to explode, network structure to become more complex and diverse, bandwidth consumption to skyrocket, and network security issues to become more prominent. The current network environment is becoming more complicated as new application services emerge on a regular basis, and different types of network application services have different QoS requirements. How to analyse and identify physical coaching videos reasonably and effectively under such complicated and changing network conditions is the key problem that we urgently need to solve right now. Because time delay and packet loss are two important factors that affect the performance of streaming media video transmission, effectively reducing time delay and packet loss rate will improve streaming media video transmission performance, allowing end users to enjoy the video quality they deserve. Because video service flow has surpassed network service flow as the most important, network video service flow is becoming increasingly important. It has significant practical implications for physical coaching video analysis and recognition using the particle swarm optimization algorithm, whether it is for improving the network’s use environment or ensuring the users’ QoE (quality of experience). The process of sports video analysis and recognition in the physical coaching process is the subject of this paper. The method studied in this paper has high action recognition rate and low false separation rate, and the recognition rate variance of all kinds of actions is low and has high stability. The initialization of particle swarm does not adopt the strategy of random generation, but sorts the feature subset according to CI, and uses the top ranked features to guide the initialization of particle swarm.



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*Source: 8189690-2022-05-04.xml*