# Accurate Recognition Method of Continuous Sports Action Based on Deep Learning Algorithm

Authors: Huang Zhengfeng
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In order to improve the recognition effect of continuous sports action, an accurate recognition method of continuous sports action based on deep learning is proposed. The convolution neural network is used to predict the key points and center vector of human body in grid mode, and the key points of human body are grouped according to the distance from the key points of the human body to the center of human body, so as to obtain the sequence diagram of sports posture, input the posture sequence diagram of sports action in the deep learning network, and apply the best strategy of continuous action recognition obtained in the deep learning training stage to the accurate recognition of continuous sports action. The position of the rectangular frame of the attitude instead of the target attitude is predicted online by the convolution neural network, and the recognition is completed when the attitude stops. The experimental results show that this method can effectively recognize continuous sports actions in real time. The expected average overlap rate of different video attributes is high, which has better recognition effect. At different position error thresholds, it has high recognition accuracy.


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## Body

## 1. Introduction

More and more attention is paid to human motion analysis in the field of computer vision. Human motion analysis is applied in the fields of intelligent visual monitoring and virtual reality [1, 2]. Therefore, studying human motion analysis methods can improve social economic benefits and has a good application prospect. Because the human body structure and posture are complicated and the theoretical limitations are high [3, 4], there are few methods that can be applied to real life.Reference [5] proposed a motion fatigue image recognition algorithm based on FPGA processor and motion image acquisition. The core technology of the image editing system based on Internet of Things is the convolution k-means (CKM) method, which is used to cluster image analysis based on statistics. The recognition is measured in the body fatigue company based on electromyography (EMG) sensor. Although this method accurately recognizes the motion fatigue image, it does not analyze the motion. Reference [6] proposes a new learning framework based on 1D-CNN architecture, which is verified by implementing very competitive results on publicly available UTD-MHAD data sets. In addition, this method provides some answers to the two biggest challenges faced by motion recognition algorithms, that is, identifying advanced activities and reducing their computing cost so that they can be accessed by embedded devices. Finally, the processability of functions in time and duration in the whole proposed framework is also studied. However, the recognition ability of this method for continuous action needs to be analyzed. A new deep multimodal feature fusion algorithm for human motion recognition is proposed in reference [7]. The key technology is a multimodel feature fusion scheme. More specifically, it integrates visual features, skeleton posture, probability map, and audio signal into a mixed feature to represent human behavior. Then, the depth model is used to optimize the combination of these feature channels, in which the weights of multiple feature channels can be predicted intelligently. Finally, the fused features are input into a multiclass support vector machine for human behavior recognition. A large number of comparison results and parameter analysis show the effectiveness of the method. However, this method has the problem of misidentification in identifying continuous actions. Reference [8] points out that human behavior analysis has always been an important aspect of social communication. Human behavior analysis is developed on the basis of human activities and behavior recognition. Human behavior recognition is an important feature in different security fields. The evaluation of motion recognition algorithms depends on the appropriate extraction and learning data. Classification plays an important role in human behavior recognition. Therefore, in order to effectively achieve this, a gated recurrent neural network is used and the computing level is improved. Feature extraction is one of the key factors of human motion recognition, which will affect the performance and computing time of the algorithm. A human behavior recognition method based on a hybrid deep learning model is proposed. The proposed method is evaluated on different data sets such as UCF sports, KTH, and UCF101. On the UCF sports data set, the proposed method gives an average of 96.8%. However, this method is prone to misidentification when identifying cumbersome continuous sports movements. Based on the above analysis, it can be seen that when recognizing continuous sports actions, due to the rapid image transformation speed, the accuracy of sports action recognition decreases and the number of time errors increases.In the field of human motion recognition, the existing works mainly focus on the analysis of RGB, depth, skeleton, and infrared data. Although these methods have noninvasive benefits, they can only be used in limited settings, prone to occlusion and other problems, and usually require a lot of computing resources. At the same time, the existing methods can not complete the tedious accurate recognition of continuous sports action. Therefore, the continuous sports action recognition method based on deep learning is studied. The action features in the continuous sports action image are extracted by using the self-coding neural network in deep learning. On this basis, the continuous sports action is accurate and real-time recognized by convolution neural network. The effectiveness of the proposed method is verified by simulation experiments.


## 2. Theoretical Basis

Deep learning is a kind of machine learning, and machine learning is the only way to realize artificial intelligence. The concept of deep learning comes from the research of the artificial neural network. Multilayer perceptron with multiple hidden layers is a deep learning structure. Deep learning combines low-level features to form more abstract high-level representation attribute categories or features, so as to find the distributed feature representation of data. The motivation of studying deep learning is to establish a neural network that simulates the human brain for analytical learning. It simulates the mechanism of the human brain to interpret data, such as images, sounds, and texts. To sum up, deep learning is a general term of pattern analysis methods. In terms of specific research content, it mainly involves three types of methods:(1)
The neural network system based on convolution operation is the convolution neural network (CNN)(2)
Self-coding neural networks based on multilayer neurons include an autoencoder and sparse coding, which have attracted extensive attention in recent years(3)
The multilayer self-coding neural network is used for pretraining and then combined with the identification information to further optimize the depth confidence network (DBN) of the neural network weightTherefore, the self-coding neural network is used to extract the image features of continuous sports action, and the convolution neural network is used to realize the recognition of sports action posture.


## 3. Extraction of Continuous Sports Action Form

Considering that the recognition accuracy of continuous sports action is reduced due to the interference of background and other factors [9], before recognition, the continuous sports action image is denoised and calibrated by adaptive threshold, and the continuous sports action form is extracted on the basis of calibration.


### 3.1. Image Preprocessing

Before feature extraction of continuous sports action image, it is necessary to preprocess the image according to relevant methods [10], so as to suppress the interference factors in the image and improve the signal-to-noise ratio of the image. In the process of image preprocessing, firstly, the continuous sports action area of the image is determined. On this basis, the contourlet transform method combined with wavelet transform is used to denoise the noise in the image. Then, the logical algorithm of sequential images is used to suppress other interference factors. Finally, the edge enhancement algorithm is used to complete the preprocessing of continuous sports action images.


#### 3.1.1. Determine the Image Continuous Sports Action Area

Because there are a large number of chaotic interference signals in the continuous sports action image, in order to avoid the influence of interference factors in the image, the image order moment is calculated according to the two-dimensional function of probability density. The continuous sports action image size mark was set ask×g, the pixel position mark as m,n, and the intensity mark as Q. The process of obtaining the adaptive calibration image is shown in
(1)jx=m∑m=0k∑n=0gQm,n,jy=n∑m=0k∑n=0gQm,n,j2x=m∑m=0k∑n=0gm−jx2Qm,n,j2y=n∑m=0k∑n=0gn−jy2Qm,n,where the position estimation result of the image is marked asjx and jy. The target distribution range of the image is marked as j2x and j2y. Finally, the target area of continuous sports action of the image is determined by the estimated position and target distribution range of the continuous sports action image.


#### 3.1.2. Continuous Sports Action Image Denoising

The contourlet transform method combined with wavelet transform [11] is used to denoise the noise in the image. The contourlet coefficient of the original continuous sports action image was set as c, the contourlet coefficient of the denoised continuous sports action image as cq, and the wavelet threshold value as Y. The wavelet threshold value of the image through the logical judgment function Px was determined. The result is shown in
(2)yc=c−sgnxYPc>Y,where the threshold function is marked asyc. Finally, the pixel information of the image is calculated through the established threshold function, so as to complete the denoising of continuous sports action image.


#### 3.1.3. Image Interference Suppression

In order to further remove other interference factors in the image, the logical algorithm based on the sequence image suppresses it. The result is shown in(3)Qt′m,n=ycminQtm,n,Qt−1m,n,where the acquisition time of continuous sports action imageQn is marked as t and the suppression result is marked as Qt′m,n.Based on the above calculation results, the pixel gray distribution of the image is obtained, and the result is shown in(4)Hm,n=Qt−1m,n+Qm,nt,where the pixel distribution of continuous sports action image is marked asHm,n and the time average of pixel gray at time t is marked as Qn−1m,n.


#### 3.1.4. Edge Enhancement of Continuous Sports Action Image

Finally, through the image edge enhancement algorithm, the edge enhancement processing of continuous sports action image is completed. The process is shown in(5)H′m,n=a+∑k=1nHm+k,n,where the gray pixel value of the continuous sports action image after image enhancement is marked asH′m,n and the constant is marked as a.


### 3.2. Feature Extraction Based on Deep Learning Self-Coding

In-depth learning, the traditional coding image edge continuous sports action form fitting degree, is poor, resulting in large error in image feature extraction results. Therefore, it is necessary to improve the coding method in-depth learning and accurately extract image features by Fisher vector coding [12], so as to obtain accurate edge continuous sports action from extraction results and improve the accuracy of continuous sports movement correction.


#### 3.2.1. Self-Coding Training

By training a deep self-coding grid, the images in training setSt1×t2 are block sampled to obtain a training set of block images [13]. The training process includes self-coding network initialization and parameter tuning. The self-coding training network is shown in Figure 1.Figure 1
Self-coding training network structure.Because a nonmultilayer self-coding network cannot use the block training set to establish a network model, the block image is randomly dividedbjj=1P through the multilayer self-coding training method. The block data set Z is used as the training parameter of the self-coding training network to obtain X=X1,X2,⋯,XNM by vectorization, where X is obtained by Z transform.Assuming that the parameter is 1001, the restricted Boltzmann machine (RBM) training process is implemented [14, 15], and the training batch size set to 52. The visible units of each restricted Boltzmann machine (RBM) operate in the 0,1.1 interval. When training the higher-level restricted Boltzmann machine (RBM), when the neuron output is close to 1, it can be considered to be in the activated state, and when it is close to 0, it can be considered to be in the inhibited state. Regardless of the top layer, the hidden units of each restricted Boltzmann machine (RBM) have nonspecified binary conditions [16, 17].


#### 3.2.2. Image Feature Extraction

According to the above image processing results and parameter settings, the image features of continuous sports action are extracted. Because the edge position of continuous sports action is more important, when extracting the image features of continuous sports action, it is necessary to calculate the closed graph composed of straight line and stripe curve in the image. Firstly, the fringe curve coordinates in the image are mapped to the high-dimensional parameter spaceEx,j based on the Hough transform method. The process is shown in
(6)Ex,j=∑j=1N∑x=0xmaxFx,j,where the slope of the linear equation of the image pixel is marked asx, the intercept is marked as j, the number of fringe curves of the image pixel is expressed as N, and the maximum slope is expressed as xmax.Both sides of the boundary based on the continuous sports action boundary in the image were searched, the edge node positionb1,b2 [18, 19] of the continuous sports action according to the minimum distance threshold was obtained, and the position coordinates of the image feature points through the coordinate curve equation were extracted. The process is shown in
(7)xu=minpi−nvi,xv=0.5npi−nvi,where the center line of the fringe in the image corresponds to the main line, the coordinates are marked asnpi and nvi, and the coordinates of the extracted feature points are marked as xu,xv.


## 3.1. Image Preprocessing

Before feature extraction of continuous sports action image, it is necessary to preprocess the image according to relevant methods [10], so as to suppress the interference factors in the image and improve the signal-to-noise ratio of the image. In the process of image preprocessing, firstly, the continuous sports action area of the image is determined. On this basis, the contourlet transform method combined with wavelet transform is used to denoise the noise in the image. Then, the logical algorithm of sequential images is used to suppress other interference factors. Finally, the edge enhancement algorithm is used to complete the preprocessing of continuous sports action images.


### 3.1.1. Determine the Image Continuous Sports Action Area

Because there are a large number of chaotic interference signals in the continuous sports action image, in order to avoid the influence of interference factors in the image, the image order moment is calculated according to the two-dimensional function of probability density. The continuous sports action image size mark was set ask×g, the pixel position mark as m,n, and the intensity mark as Q. The process of obtaining the adaptive calibration image is shown in
(1)jx=m∑m=0k∑n=0gQm,n,jy=n∑m=0k∑n=0gQm,n,j2x=m∑m=0k∑n=0gm−jx2Qm,n,j2y=n∑m=0k∑n=0gn−jy2Qm,n,where the position estimation result of the image is marked asjx and jy. The target distribution range of the image is marked as j2x and j2y. Finally, the target area of continuous sports action of the image is determined by the estimated position and target distribution range of the continuous sports action image.


### 3.1.2. Continuous Sports Action Image Denoising

The contourlet transform method combined with wavelet transform [11] is used to denoise the noise in the image. The contourlet coefficient of the original continuous sports action image was set as c, the contourlet coefficient of the denoised continuous sports action image as cq, and the wavelet threshold value as Y. The wavelet threshold value of the image through the logical judgment function Px was determined. The result is shown in
(2)yc=c−sgnxYPc>Y,where the threshold function is marked asyc. Finally, the pixel information of the image is calculated through the established threshold function, so as to complete the denoising of continuous sports action image.


### 3.1.3. Image Interference Suppression

In order to further remove other interference factors in the image, the logical algorithm based on the sequence image suppresses it. The result is shown in(3)Qt′m,n=ycminQtm,n,Qt−1m,n,where the acquisition time of continuous sports action imageQn is marked as t and the suppression result is marked as Qt′m,n.Based on the above calculation results, the pixel gray distribution of the image is obtained, and the result is shown in(4)Hm,n=Qt−1m,n+Qm,nt,where the pixel distribution of continuous sports action image is marked asHm,n and the time average of pixel gray at time t is marked as Qn−1m,n.


### 3.1.4. Edge Enhancement of Continuous Sports Action Image

Finally, through the image edge enhancement algorithm, the edge enhancement processing of continuous sports action image is completed. The process is shown in(5)H′m,n=a+∑k=1nHm+k,n,where the gray pixel value of the continuous sports action image after image enhancement is marked asH′m,n and the constant is marked as a.


## 3.1.1. Determine the Image Continuous Sports Action Area

Because there are a large number of chaotic interference signals in the continuous sports action image, in order to avoid the influence of interference factors in the image, the image order moment is calculated according to the two-dimensional function of probability density. The continuous sports action image size mark was set ask×g, the pixel position mark as m,n, and the intensity mark as Q. The process of obtaining the adaptive calibration image is shown in
(1)jx=m∑m=0k∑n=0gQm,n,jy=n∑m=0k∑n=0gQm,n,j2x=m∑m=0k∑n=0gm−jx2Qm,n,j2y=n∑m=0k∑n=0gn−jy2Qm,n,where the position estimation result of the image is marked asjx and jy. The target distribution range of the image is marked as j2x and j2y. Finally, the target area of continuous sports action of the image is determined by the estimated position and target distribution range of the continuous sports action image.


## 3.1.2. Continuous Sports Action Image Denoising

The contourlet transform method combined with wavelet transform [11] is used to denoise the noise in the image. The contourlet coefficient of the original continuous sports action image was set as c, the contourlet coefficient of the denoised continuous sports action image as cq, and the wavelet threshold value as Y. The wavelet threshold value of the image through the logical judgment function Px was determined. The result is shown in
(2)yc=c−sgnxYPc>Y,where the threshold function is marked asyc. Finally, the pixel information of the image is calculated through the established threshold function, so as to complete the denoising of continuous sports action image.


## 3.1.3. Image Interference Suppression

In order to further remove other interference factors in the image, the logical algorithm based on the sequence image suppresses it. The result is shown in(3)Qt′m,n=ycminQtm,n,Qt−1m,n,where the acquisition time of continuous sports action imageQn is marked as t and the suppression result is marked as Qt′m,n.Based on the above calculation results, the pixel gray distribution of the image is obtained, and the result is shown in(4)Hm,n=Qt−1m,n+Qm,nt,where the pixel distribution of continuous sports action image is marked asHm,n and the time average of pixel gray at time t is marked as Qn−1m,n.


## 3.1.4. Edge Enhancement of Continuous Sports Action Image

Finally, through the image edge enhancement algorithm, the edge enhancement processing of continuous sports action image is completed. The process is shown in(5)H′m,n=a+∑k=1nHm+k,n,where the gray pixel value of the continuous sports action image after image enhancement is marked asH′m,n and the constant is marked as a.


## 3.2. Feature Extraction Based on Deep Learning Self-Coding

In-depth learning, the traditional coding image edge continuous sports action form fitting degree, is poor, resulting in large error in image feature extraction results. Therefore, it is necessary to improve the coding method in-depth learning and accurately extract image features by Fisher vector coding [12], so as to obtain accurate edge continuous sports action from extraction results and improve the accuracy of continuous sports movement correction.


### 3.2.1. Self-Coding Training

By training a deep self-coding grid, the images in training setSt1×t2 are block sampled to obtain a training set of block images [13]. The training process includes self-coding network initialization and parameter tuning. The self-coding training network is shown in Figure 1.Figure 1
Self-coding training network structure.Because a nonmultilayer self-coding network cannot use the block training set to establish a network model, the block image is randomly dividedbjj=1P through the multilayer self-coding training method. The block data set Z is used as the training parameter of the self-coding training network to obtain X=X1,X2,⋯,XNM by vectorization, where X is obtained by Z transform.Assuming that the parameter is 1001, the restricted Boltzmann machine (RBM) training process is implemented [14, 15], and the training batch size set to 52. The visible units of each restricted Boltzmann machine (RBM) operate in the 0,1.1 interval. When training the higher-level restricted Boltzmann machine (RBM), when the neuron output is close to 1, it can be considered to be in the activated state, and when it is close to 0, it can be considered to be in the inhibited state. Regardless of the top layer, the hidden units of each restricted Boltzmann machine (RBM) have nonspecified binary conditions [16, 17].


### 3.2.2. Image Feature Extraction

According to the above image processing results and parameter settings, the image features of continuous sports action are extracted. Because the edge position of continuous sports action is more important, when extracting the image features of continuous sports action, it is necessary to calculate the closed graph composed of straight line and stripe curve in the image. Firstly, the fringe curve coordinates in the image are mapped to the high-dimensional parameter spaceEx,j based on the Hough transform method. The process is shown in
(6)Ex,j=∑j=1N∑x=0xmaxFx,j,where the slope of the linear equation of the image pixel is marked asx, the intercept is marked as j, the number of fringe curves of the image pixel is expressed as N, and the maximum slope is expressed as xmax.Both sides of the boundary based on the continuous sports action boundary in the image were searched, the edge node positionb1,b2 [18, 19] of the continuous sports action according to the minimum distance threshold was obtained, and the position coordinates of the image feature points through the coordinate curve equation were extracted. The process is shown in
(7)xu=minpi−nvi,xv=0.5npi−nvi,where the center line of the fringe in the image corresponds to the main line, the coordinates are marked asnpi and nvi, and the coordinates of the extracted feature points are marked as xu,xv.


## 3.2.1. Self-Coding Training

By training a deep self-coding grid, the images in training setSt1×t2 are block sampled to obtain a training set of block images [13]. The training process includes self-coding network initialization and parameter tuning. The self-coding training network is shown in Figure 1.Figure 1
Self-coding training network structure.Because a nonmultilayer self-coding network cannot use the block training set to establish a network model, the block image is randomly dividedbjj=1P through the multilayer self-coding training method. The block data set Z is used as the training parameter of the self-coding training network to obtain X=X1,X2,⋯,XNM by vectorization, where X is obtained by Z transform.Assuming that the parameter is 1001, the restricted Boltzmann machine (RBM) training process is implemented [14, 15], and the training batch size set to 52. The visible units of each restricted Boltzmann machine (RBM) operate in the 0,1.1 interval. When training the higher-level restricted Boltzmann machine (RBM), when the neuron output is close to 1, it can be considered to be in the activated state, and when it is close to 0, it can be considered to be in the inhibited state. Regardless of the top layer, the hidden units of each restricted Boltzmann machine (RBM) have nonspecified binary conditions [16, 17].


## 3.2.2. Image Feature Extraction

According to the above image processing results and parameter settings, the image features of continuous sports action are extracted. Because the edge position of continuous sports action is more important, when extracting the image features of continuous sports action, it is necessary to calculate the closed graph composed of straight line and stripe curve in the image. Firstly, the fringe curve coordinates in the image are mapped to the high-dimensional parameter spaceEx,j based on the Hough transform method. The process is shown in
(6)Ex,j=∑j=1N∑x=0xmaxFx,j,where the slope of the linear equation of the image pixel is marked asx, the intercept is marked as j, the number of fringe curves of the image pixel is expressed as N, and the maximum slope is expressed as xmax.Both sides of the boundary based on the continuous sports action boundary in the image were searched, the edge node positionb1,b2 [18, 19] of the continuous sports action according to the minimum distance threshold was obtained, and the position coordinates of the image feature points through the coordinate curve equation were extracted. The process is shown in
(7)xu=minpi−nvi,xv=0.5npi−nvi,where the center line of the fringe in the image corresponds to the main line, the coordinates are marked asnpi and nvi, and the coordinates of the extracted feature points are marked as xu,xv.


## 4. Design of Accurate Recognition Method for Continuous Sports Action

Based on the above continuous sports action shape extraction results, the convolution neural network in deep learning is used to design the accurate recognition method of continuous sports action.


### 4.1. Sports Posture Estimation Algorithm Based on Convolutional Neural Network

Convolutional neural networks (CNN) are a kind of feedforward neural networks with depth structure including convolution calculation. It is one of the representative algorithms of deep learning. The convolution neural network is constructed by imitating the visual perception mechanism of biology, which can carry out supervised learning and unsupervised learning. The parameter sharing of convolution kernel in its hidden layer and the sparsity of interlayer connection enable the convolution neural network to learn grid-like topology features, such as pixels and audio, with less computation. It has a stable effect and has no additional feature engineering requirements for data. Therefore, the convolutional neural network is used to predict the key points and center vectors of the human body in a grid way, make the key points of the human body point to the center of the human body according to the center vector, group the key points of the human body according to the distance from the key points of the human body to the center of the human body, complete the estimation of sports action posture [20], and obtain the sequence diagram of sports action posture, so as to complete the accurate recognition of continuous sports movements.


#### 4.1.1. Human Body Structure Characteristics and Center Vector

Make the coordinates of thezth human body key point of the human body be xk,yk and the human body center be xc,yc. The positional relationship between xk,yk and xc,yc is established through the direction and distance. The direction and distance, i.e., the center vector, are depicted by the two-dimensional vector ux,z,uy,z. The direction parallel to the ground is ux,z, and the direction perpendicular to the ground is uy,z. The formula is shown in
(8)ux,z=xcW−xzW,(9)uy,z=ycH−yzH,where the width and height of the image areW and H, and formulas (8) and (9) are used to normalize the processing distance to keep the distance between −1,1.


#### 4.1.2. Sports Posture Estimation Based on CNN

CNN is used to predictux,z,uy,z in grid mode and establish the connection relationship between xz,yz and xc,yc. The extraction part of ux,z,uy,z is constructed by a three-layer convolution activation function. The first two layers use RELU activation function and the last layer uses tanh activation function to ensure that the value of learning fitting ux,z,uy,z remains between -1 and 1. The loss function of the network includes the probability loss lconf and the offset position loss lloc of xz,yz, so that the loss of ux,z,uy,z is lvec, which is used to supervise and learn the position coordinate ux,z,uy,z of xz,yz pointing to the center of the human body, and lvec belongs to the mean square error loss. The calculation formulas of lconf, lloc, and lvec are shown in
(10)lconf=ωobj+ωnoobj⋅∑z=1Z∑j=1s21z,jobjpzj2,(11)lloc=ωxy⋅∑z=1Z∑j=1s21z,jobjx~x,zj−x~∧x,zj2+ωvec⋅∑z=1Z∑j=1s21z,jobjy~y,zj−y~∧y,zj2,(12)lvec=ωvec⋅∑z=1Z∑j=1s21z,jobjux,zj−u∧x,zj2+ωvec⋅∑z=1Z∑j=1s21z,jobjuy,zj−u∧y,zj2,where the grid number iss2. The direction of xz,yz and ux,z,uy,z connection vectors parallel to the ground in the jth grid is u^x,kj. The corresponding label of u^x,kj is ux,kj. The direction of xz,yz and ux,z,uy,z connection vectors in the jth grid perpendicular to the ground is u^y,kj. The corresponding label of u^y,kj is uy,kj. The prediction probability of xz,yz is p^zj. The corresponding label of xz,yz is pzj. The predicted lateral and longitudinal position offset of xz,yz is x~^x,zj, y~^y,zj. The corresponding labels of x~^x,zj are x~x,zj and y~y,zj. The probability loss of positive and negative samples is ωobj and ωnoobj. The weight of offset position loss in the total loss is ωxy. The weight of vector loss in the total loss is ωvec. The indicative functions are 1z,jobj and 1z,jnoobj. When 1z,jobj=1, it indicates that the zth type xz,yz is in the jth grid, and when 1z,jobj=0, it indicates that the zth type xz,yz is in other grids. When 1z,jnoobj=1, it indicates that class zxz,yz is not in the jth grid, and when 1z,jobj=0, it indicates that class zxz,yz is not in other grids. The total loss formula is shown in
(13)loss=lconf+lloc+lvec.


#### 4.1.3. Sports Posture Estimation Process

The trained sports posture estimation network is obtained through supervised learning. The specific prediction estimation is divided into the network solution part and groupxz,yz according to the characteristics of human body structure. The specific process is as follows:Step 1: input the image in the network and obtainxz,yz and ux,z,uy,z by using the sports action posture estimation network.Step 2: obtain thexz,yz of multiple similar human bodies through the nonmaximum suppression method. After locating a probability peak point, this method can control the probability values of other fields to be lower than this value, so as to avoid repeated detection of the posture xz,yz of the same human body [21].Step 3: extract the predictedu^x,z,u^y,z of the grid where x^z,y^z is located and use formulas (14) and (15) to point x^z,y^z to the human body center xz′,yz′.
(14)xz′=u^x,z×W+x^z,(15)yz′=u^y,z×H+y^z.Step 4: analyze the distance fromx^z,y^z to xz′,yz′, and the human body key points close to each other are the same human body. Take the xz′,yz′ pointed by each x^z,y^z as the feature, use the unsupervised clustering algorithm [22, 23] to group all x^z,y^z, construct a sports action posture through the x^z,y^z of each group, complete the sports action posture estimation [24], and obtain the sports action posture video sequence q.


### 4.2. Continuous Motion Recognition Method Based on Deep Learning

CNN is used to obtainq and input into the deep learning network to realize real-time recognition of continuous sports actions. The specific recognition process is shown in Figure 2.Figure 2
Continuous sports action recognition process.The specific steps of continuous sports action recognition method based on deep learning are as follows:Step 1: train CNN offline, input the sports posture in the network, estimate theQ obtained by CNN, output reasonable posture and confidence, and output posture labels and category labels through supervised learning and training stage; use the deep learning training stage to train the softmax11 layer, that is, the action network, to predict the best posture [25, 26]. In the deep learning training stage, maximize the cumulative reward in Q and ensure that the network learns the best strategy for continuous action recognition in the video sequence.Step 2: apply the best strategy obtained from offline training to the accurate recognition of continuous sports actions. Continuous sports actions accurately recognize the final position ofF−1 frame and regard it as the initial state of the target position of F frame, so as to improve the robustness of multiperson continuous action recognition [27].Step 3: a series of attitudes predicted by CNN replace the rectangular frame position of the target attitude. When the network predicts that the attitude stops, the current frame completes the recognition. At this time, the rectangular frame position is the recognition result of multiple continuous actions in the current frame.


#### 4.2.1. Offline Training

Offline training includes sample image blockqα, attitude label oαact, and category label oαcls. Gaussian noise is introduced into the actual posture position of each sports posture video frame to obtain its corresponding qα. The attitude tag acquisition formula corresponding to each positive qα is shown in
(16)θαact=argmaxaIOUf¯qα,a,G,where the actual image block isG. The new image block obtained after the movement of attitude a in q is f¯qα,a. The overlap rate is IOU.θαcls is obtained according to the IOU of each G and the actual position of the target, and the formula is shown in
(17)θαcls=1,ifIOUqα,G≥0.8,0,ifIOUqα,G<0.8,where the target attitude category is 1. The background category is 0.After obtaining the training samples, the supervised learning training is completed by using the multitask loss functionLSL [28, 29], and the formula is shown in
(18)LSL=1m∑α=1mLθαact,θ^αact+∑α=βmLθαcls,θ^αcls,where the number of samples of each training batchqα,θαact,θαclsα−1m is m. The predicted attitude and category are θ^αact and θ^αcls. The random gradient descent method is used to minimize LSL training CNN to obtain the network parameter CSL in the supervised learning stage.


#### 4.2.2. In-Depth Learning and Training

Carry out in-depth learning training for any attitude video sequence with lengthN and the corresponding actual value, so that the state of a series of sequences is vt,n, and the corresponding attitude and reward information is at,n and rvt,n, where the number of iterations is t=1,2,⋯,Tn and n=1,2,⋯,N. In case of vt,n, the corresponding at,n is expressed as
(19)bt,n=argmaxbqast,n;CRL,where the deep learning strategy isqavt,n. The update parameter of the deep learning training network is CRL.To maximize the cumulative reward updateCRL, the formula is shown in
(20)ΔCRL∝∑nN∑tTn∂lgqavt,n;CRLrt,n∂CRL,where the reward value of each frame isrt,n.In the deep learning training stage, the bestqavt,n [30] is obtained by the strategy gradient method, and a strategy π is arbitrarily set to obtain the probability distribution of st corresponding to at, i.e., δav;w. The parameter is w; a complete sequence of v, a and total reward Rτ was set. Order R=∑t=0T−1rt, τ is expressed as
(21)τ=s0,a0,r0,s1,a1,r1,⋯,sT−1,aT−1,rT−1.The policy gradient ofR is as [31, 32]
(22)g=R∇w∑t=0T−1logδav;w.Formula (22) updates the deep learning strategy according to formula (23), and the formula is as
(23)w⟵w+γg,where the learning rate isγ. In formula (22), the gradient term represents a large cumulative reward that can be improved τ. In the direction of occurrence times, continuously optimize the strategy in the form of gradient rise to obtain the best strategy.


#### 4.2.3. Accurate Identification of Continuous Sports Movements

After thef frame recognition is completed, the rectangular box position output by the deep learning network is the probability of the target attitude (background) category, that is, the confidence score b of the target attitude. The specified threshold is exceeded at b>μ. If the recognition result of f frame continuous sports action is reliable, the recognition result is generated into a training sample for online fine-tuning; On the contrary, it represents that the target attitude is lost [33, 34], and the recognition result is unreliable. It is necessary to reclip the target attitude information of frame f, form U candidate target attitude positions d~ββ=1U in the adjacent position of the target through random Gaussian noise, input them into the network, and take the re obtained target attitude position d∗ as the new target attitude position of the frame as the training sample during fine-tuning [35, 36]. The expression formula of is as
(24)d∗=argmaxd~βbd~β,where the candidate target attitude position corresponding to the maximumb belongs to the current target attitude position, which is used to obtain the training samples in the online fine-tuning process.


## 4.1. Sports Posture Estimation Algorithm Based on Convolutional Neural Network

Convolutional neural networks (CNN) are a kind of feedforward neural networks with depth structure including convolution calculation. It is one of the representative algorithms of deep learning. The convolution neural network is constructed by imitating the visual perception mechanism of biology, which can carry out supervised learning and unsupervised learning. The parameter sharing of convolution kernel in its hidden layer and the sparsity of interlayer connection enable the convolution neural network to learn grid-like topology features, such as pixels and audio, with less computation. It has a stable effect and has no additional feature engineering requirements for data. Therefore, the convolutional neural network is used to predict the key points and center vectors of the human body in a grid way, make the key points of the human body point to the center of the human body according to the center vector, group the key points of the human body according to the distance from the key points of the human body to the center of the human body, complete the estimation of sports action posture [20], and obtain the sequence diagram of sports action posture, so as to complete the accurate recognition of continuous sports movements.


### 4.1.1. Human Body Structure Characteristics and Center Vector

Make the coordinates of thezth human body key point of the human body be xk,yk and the human body center be xc,yc. The positional relationship between xk,yk and xc,yc is established through the direction and distance. The direction and distance, i.e., the center vector, are depicted by the two-dimensional vector ux,z,uy,z. The direction parallel to the ground is ux,z, and the direction perpendicular to the ground is uy,z. The formula is shown in
(8)ux,z=xcW−xzW,(9)uy,z=ycH−yzH,where the width and height of the image areW and H, and formulas (8) and (9) are used to normalize the processing distance to keep the distance between −1,1.


### 4.1.2. Sports Posture Estimation Based on CNN

CNN is used to predictux,z,uy,z in grid mode and establish the connection relationship between xz,yz and xc,yc. The extraction part of ux,z,uy,z is constructed by a three-layer convolution activation function. The first two layers use RELU activation function and the last layer uses tanh activation function to ensure that the value of learning fitting ux,z,uy,z remains between -1 and 1. The loss function of the network includes the probability loss lconf and the offset position loss lloc of xz,yz, so that the loss of ux,z,uy,z is lvec, which is used to supervise and learn the position coordinate ux,z,uy,z of xz,yz pointing to the center of the human body, and lvec belongs to the mean square error loss. The calculation formulas of lconf, lloc, and lvec are shown in
(10)lconf=ωobj+ωnoobj⋅∑z=1Z∑j=1s21z,jobjpzj2,(11)lloc=ωxy⋅∑z=1Z∑j=1s21z,jobjx~x,zj−x~∧x,zj2+ωvec⋅∑z=1Z∑j=1s21z,jobjy~y,zj−y~∧y,zj2,(12)lvec=ωvec⋅∑z=1Z∑j=1s21z,jobjux,zj−u∧x,zj2+ωvec⋅∑z=1Z∑j=1s21z,jobjuy,zj−u∧y,zj2,where the grid number iss2. The direction of xz,yz and ux,z,uy,z connection vectors parallel to the ground in the jth grid is u^x,kj. The corresponding label of u^x,kj is ux,kj. The direction of xz,yz and ux,z,uy,z connection vectors in the jth grid perpendicular to the ground is u^y,kj. The corresponding label of u^y,kj is uy,kj. The prediction probability of xz,yz is p^zj. The corresponding label of xz,yz is pzj. The predicted lateral and longitudinal position offset of xz,yz is x~^x,zj, y~^y,zj. The corresponding labels of x~^x,zj are x~x,zj and y~y,zj. The probability loss of positive and negative samples is ωobj and ωnoobj. The weight of offset position loss in the total loss is ωxy. The weight of vector loss in the total loss is ωvec. The indicative functions are 1z,jobj and 1z,jnoobj. When 1z,jobj=1, it indicates that the zth type xz,yz is in the jth grid, and when 1z,jobj=0, it indicates that the zth type xz,yz is in other grids. When 1z,jnoobj=1, it indicates that class zxz,yz is not in the jth grid, and when 1z,jobj=0, it indicates that class zxz,yz is not in other grids. The total loss formula is shown in
(13)loss=lconf+lloc+lvec.


### 4.1.3. Sports Posture Estimation Process

The trained sports posture estimation network is obtained through supervised learning. The specific prediction estimation is divided into the network solution part and groupxz,yz according to the characteristics of human body structure. The specific process is as follows:Step 1: input the image in the network and obtainxz,yz and ux,z,uy,z by using the sports action posture estimation network.Step 2: obtain thexz,yz of multiple similar human bodies through the nonmaximum suppression method. After locating a probability peak point, this method can control the probability values of other fields to be lower than this value, so as to avoid repeated detection of the posture xz,yz of the same human body [21].Step 3: extract the predictedu^x,z,u^y,z of the grid where x^z,y^z is located and use formulas (14) and (15) to point x^z,y^z to the human body center xz′,yz′.
(14)xz′=u^x,z×W+x^z,(15)yz′=u^y,z×H+y^z.Step 4: analyze the distance fromx^z,y^z to xz′,yz′, and the human body key points close to each other are the same human body. Take the xz′,yz′ pointed by each x^z,y^z as the feature, use the unsupervised clustering algorithm [22, 23] to group all x^z,y^z, construct a sports action posture through the x^z,y^z of each group, complete the sports action posture estimation [24], and obtain the sports action posture video sequence q.


## 4.1.1. Human Body Structure Characteristics and Center Vector

Make the coordinates of thezth human body key point of the human body be xk,yk and the human body center be xc,yc. The positional relationship between xk,yk and xc,yc is established through the direction and distance. The direction and distance, i.e., the center vector, are depicted by the two-dimensional vector ux,z,uy,z. The direction parallel to the ground is ux,z, and the direction perpendicular to the ground is uy,z. The formula is shown in
(8)ux,z=xcW−xzW,(9)uy,z=ycH−yzH,where the width and height of the image areW and H, and formulas (8) and (9) are used to normalize the processing distance to keep the distance between −1,1.


## 4.1.2. Sports Posture Estimation Based on CNN

CNN is used to predictux,z,uy,z in grid mode and establish the connection relationship between xz,yz and xc,yc. The extraction part of ux,z,uy,z is constructed by a three-layer convolution activation function. The first two layers use RELU activation function and the last layer uses tanh activation function to ensure that the value of learning fitting ux,z,uy,z remains between -1 and 1. The loss function of the network includes the probability loss lconf and the offset position loss lloc of xz,yz, so that the loss of ux,z,uy,z is lvec, which is used to supervise and learn the position coordinate ux,z,uy,z of xz,yz pointing to the center of the human body, and lvec belongs to the mean square error loss. The calculation formulas of lconf, lloc, and lvec are shown in
(10)lconf=ωobj+ωnoobj⋅∑z=1Z∑j=1s21z,jobjpzj2,(11)lloc=ωxy⋅∑z=1Z∑j=1s21z,jobjx~x,zj−x~∧x,zj2+ωvec⋅∑z=1Z∑j=1s21z,jobjy~y,zj−y~∧y,zj2,(12)lvec=ωvec⋅∑z=1Z∑j=1s21z,jobjux,zj−u∧x,zj2+ωvec⋅∑z=1Z∑j=1s21z,jobjuy,zj−u∧y,zj2,where the grid number iss2. The direction of xz,yz and ux,z,uy,z connection vectors parallel to the ground in the jth grid is u^x,kj. The corresponding label of u^x,kj is ux,kj. The direction of xz,yz and ux,z,uy,z connection vectors in the jth grid perpendicular to the ground is u^y,kj. The corresponding label of u^y,kj is uy,kj. The prediction probability of xz,yz is p^zj. The corresponding label of xz,yz is pzj. The predicted lateral and longitudinal position offset of xz,yz is x~^x,zj, y~^y,zj. The corresponding labels of x~^x,zj are x~x,zj and y~y,zj. The probability loss of positive and negative samples is ωobj and ωnoobj. The weight of offset position loss in the total loss is ωxy. The weight of vector loss in the total loss is ωvec. The indicative functions are 1z,jobj and 1z,jnoobj. When 1z,jobj=1, it indicates that the zth type xz,yz is in the jth grid, and when 1z,jobj=0, it indicates that the zth type xz,yz is in other grids. When 1z,jnoobj=1, it indicates that class zxz,yz is not in the jth grid, and when 1z,jobj=0, it indicates that class zxz,yz is not in other grids. The total loss formula is shown in
(13)loss=lconf+lloc+lvec.


## 4.1.3. Sports Posture Estimation Process

The trained sports posture estimation network is obtained through supervised learning. The specific prediction estimation is divided into the network solution part and groupxz,yz according to the characteristics of human body structure. The specific process is as follows:Step 1: input the image in the network and obtainxz,yz and ux,z,uy,z by using the sports action posture estimation network.Step 2: obtain thexz,yz of multiple similar human bodies through the nonmaximum suppression method. After locating a probability peak point, this method can control the probability values of other fields to be lower than this value, so as to avoid repeated detection of the posture xz,yz of the same human body [21].Step 3: extract the predictedu^x,z,u^y,z of the grid where x^z,y^z is located and use formulas (14) and (15) to point x^z,y^z to the human body center xz′,yz′.
(14)xz′=u^x,z×W+x^z,(15)yz′=u^y,z×H+y^z.Step 4: analyze the distance fromx^z,y^z to xz′,yz′, and the human body key points close to each other are the same human body. Take the xz′,yz′ pointed by each x^z,y^z as the feature, use the unsupervised clustering algorithm [22, 23] to group all x^z,y^z, construct a sports action posture through the x^z,y^z of each group, complete the sports action posture estimation [24], and obtain the sports action posture video sequence q.


## 4.2. Continuous Motion Recognition Method Based on Deep Learning

CNN is used to obtainq and input into the deep learning network to realize real-time recognition of continuous sports actions. The specific recognition process is shown in Figure 2.Figure 2
Continuous sports action recognition process.The specific steps of continuous sports action recognition method based on deep learning are as follows:Step 1: train CNN offline, input the sports posture in the network, estimate theQ obtained by CNN, output reasonable posture and confidence, and output posture labels and category labels through supervised learning and training stage; use the deep learning training stage to train the softmax11 layer, that is, the action network, to predict the best posture [25, 26]. In the deep learning training stage, maximize the cumulative reward in Q and ensure that the network learns the best strategy for continuous action recognition in the video sequence.Step 2: apply the best strategy obtained from offline training to the accurate recognition of continuous sports actions. Continuous sports actions accurately recognize the final position ofF−1 frame and regard it as the initial state of the target position of F frame, so as to improve the robustness of multiperson continuous action recognition [27].Step 3: a series of attitudes predicted by CNN replace the rectangular frame position of the target attitude. When the network predicts that the attitude stops, the current frame completes the recognition. At this time, the rectangular frame position is the recognition result of multiple continuous actions in the current frame.


### 4.2.1. Offline Training

Offline training includes sample image blockqα, attitude label oαact, and category label oαcls. Gaussian noise is introduced into the actual posture position of each sports posture video frame to obtain its corresponding qα. The attitude tag acquisition formula corresponding to each positive qα is shown in
(16)θαact=argmaxaIOUf¯qα,a,G,where the actual image block isG. The new image block obtained after the movement of attitude a in q is f¯qα,a. The overlap rate is IOU.θαcls is obtained according to the IOU of each G and the actual position of the target, and the formula is shown in
(17)θαcls=1,ifIOUqα,G≥0.8,0,ifIOUqα,G<0.8,where the target attitude category is 1. The background category is 0.After obtaining the training samples, the supervised learning training is completed by using the multitask loss functionLSL [28, 29], and the formula is shown in
(18)LSL=1m∑α=1mLθαact,θ^αact+∑α=βmLθαcls,θ^αcls,where the number of samples of each training batchqα,θαact,θαclsα−1m is m. The predicted attitude and category are θ^αact and θ^αcls. The random gradient descent method is used to minimize LSL training CNN to obtain the network parameter CSL in the supervised learning stage.


### 4.2.2. In-Depth Learning and Training

Carry out in-depth learning training for any attitude video sequence with lengthN and the corresponding actual value, so that the state of a series of sequences is vt,n, and the corresponding attitude and reward information is at,n and rvt,n, where the number of iterations is t=1,2,⋯,Tn and n=1,2,⋯,N. In case of vt,n, the corresponding at,n is expressed as
(19)bt,n=argmaxbqast,n;CRL,where the deep learning strategy isqavt,n. The update parameter of the deep learning training network is CRL.To maximize the cumulative reward updateCRL, the formula is shown in
(20)ΔCRL∝∑nN∑tTn∂lgqavt,n;CRLrt,n∂CRL,where the reward value of each frame isrt,n.In the deep learning training stage, the bestqavt,n [30] is obtained by the strategy gradient method, and a strategy π is arbitrarily set to obtain the probability distribution of st corresponding to at, i.e., δav;w. The parameter is w; a complete sequence of v, a and total reward Rτ was set. Order R=∑t=0T−1rt, τ is expressed as
(21)τ=s0,a0,r0,s1,a1,r1,⋯,sT−1,aT−1,rT−1.The policy gradient ofR is as [31, 32]
(22)g=R∇w∑t=0T−1logδav;w.Formula (22) updates the deep learning strategy according to formula (23), and the formula is as
(23)w⟵w+γg,where the learning rate isγ. In formula (22), the gradient term represents a large cumulative reward that can be improved τ. In the direction of occurrence times, continuously optimize the strategy in the form of gradient rise to obtain the best strategy.


### 4.2.3. Accurate Identification of Continuous Sports Movements

After thef frame recognition is completed, the rectangular box position output by the deep learning network is the probability of the target attitude (background) category, that is, the confidence score b of the target attitude. The specified threshold is exceeded at b>μ. If the recognition result of f frame continuous sports action is reliable, the recognition result is generated into a training sample for online fine-tuning; On the contrary, it represents that the target attitude is lost [33, 34], and the recognition result is unreliable. It is necessary to reclip the target attitude information of frame f, form U candidate target attitude positions d~ββ=1U in the adjacent position of the target through random Gaussian noise, input them into the network, and take the re obtained target attitude position d∗ as the new target attitude position of the frame as the training sample during fine-tuning [35, 36]. The expression formula of is as
(24)d∗=argmaxd~βbd~β,where the candidate target attitude position corresponding to the maximumb belongs to the current target attitude position, which is used to obtain the training samples in the online fine-tuning process.


## 4.2.1. Offline Training

Offline training includes sample image blockqα, attitude label oαact, and category label oαcls. Gaussian noise is introduced into the actual posture position of each sports posture video frame to obtain its corresponding qα. The attitude tag acquisition formula corresponding to each positive qα is shown in
(16)θαact=argmaxaIOUf¯qα,a,G,where the actual image block isG. The new image block obtained after the movement of attitude a in q is f¯qα,a. The overlap rate is IOU.θαcls is obtained according to the IOU of each G and the actual position of the target, and the formula is shown in
(17)θαcls=1,ifIOUqα,G≥0.8,0,ifIOUqα,G<0.8,where the target attitude category is 1. The background category is 0.After obtaining the training samples, the supervised learning training is completed by using the multitask loss functionLSL [28, 29], and the formula is shown in
(18)LSL=1m∑α=1mLθαact,θ^αact+∑α=βmLθαcls,θ^αcls,where the number of samples of each training batchqα,θαact,θαclsα−1m is m. The predicted attitude and category are θ^αact and θ^αcls. The random gradient descent method is used to minimize LSL training CNN to obtain the network parameter CSL in the supervised learning stage.


## 4.2.2. In-Depth Learning and Training

Carry out in-depth learning training for any attitude video sequence with lengthN and the corresponding actual value, so that the state of a series of sequences is vt,n, and the corresponding attitude and reward information is at,n and rvt,n, where the number of iterations is t=1,2,⋯,Tn and n=1,2,⋯,N. In case of vt,n, the corresponding at,n is expressed as
(19)bt,n=argmaxbqast,n;CRL,where the deep learning strategy isqavt,n. The update parameter of the deep learning training network is CRL.To maximize the cumulative reward updateCRL, the formula is shown in
(20)ΔCRL∝∑nN∑tTn∂lgqavt,n;CRLrt,n∂CRL,where the reward value of each frame isrt,n.In the deep learning training stage, the bestqavt,n [30] is obtained by the strategy gradient method, and a strategy π is arbitrarily set to obtain the probability distribution of st corresponding to at, i.e., δav;w. The parameter is w; a complete sequence of v, a and total reward Rτ was set. Order R=∑t=0T−1rt, τ is expressed as
(21)τ=s0,a0,r0,s1,a1,r1,⋯,sT−1,aT−1,rT−1.The policy gradient ofR is as [31, 32]
(22)g=R∇w∑t=0T−1logδav;w.Formula (22) updates the deep learning strategy according to formula (23), and the formula is as
(23)w⟵w+γg,where the learning rate isγ. In formula (22), the gradient term represents a large cumulative reward that can be improved τ. In the direction of occurrence times, continuously optimize the strategy in the form of gradient rise to obtain the best strategy.


## 4.2.3. Accurate Identification of Continuous Sports Movements

After thef frame recognition is completed, the rectangular box position output by the deep learning network is the probability of the target attitude (background) category, that is, the confidence score b of the target attitude. The specified threshold is exceeded at b>μ. If the recognition result of f frame continuous sports action is reliable, the recognition result is generated into a training sample for online fine-tuning; On the contrary, it represents that the target attitude is lost [33, 34], and the recognition result is unreliable. It is necessary to reclip the target attitude information of frame f, form U candidate target attitude positions d~ββ=1U in the adjacent position of the target through random Gaussian noise, input them into the network, and take the re obtained target attitude position d∗ as the new target attitude position of the frame as the training sample during fine-tuning [35, 36]. The expression formula of is as
(24)d∗=argmaxd~βbd~β,where the candidate target attitude position corresponding to the maximumb belongs to the current target attitude position, which is used to obtain the training samples in the online fine-tuning process.


## 5. Experimental Results and Comparative Analysis

In order to verify the effectiveness of the proposed method, several simulation experiments were carried out in the MATLAB environment to verify the recognition performance of continuous sports movement.


### 5.1. Experimental Platform

The server used in the experiment is Intel Xeon (R) CPU e5-2650 v4@2.20 Hz x4s/12 GB geforce gtx1080ti x2 GPU, 64 GB running memory, Ubuntu &amp; 04 operating system and tensorhow0 deep learning framework.After the preparation of the hardware platform, 500 continuous sports action images were collected, and the random gradient descent method was selected to train the CNN algorithm in an end-to-end joint way. Set the learning rate as 0.0087, the attenuation coefficient of the weight as 0.0001, the number of iterations as 2000, save the model once per iteration, and finally select the model with the highest accuracy. According to this process, 1000 training set sample data are used for training, and 2000 iterations are performed on the above training set. Training 500 images takes 24 hours, and the training accuracy loss curve is shown in Figure3.Figure 3
Training loss curve.It can be seen from Figure3 that with the increasing number of iterations, the accuracy loss caused by the training set gradually decreases, indicating that the model training effect is good. The training loss basically converges to a stable value, indicating that the expected training effect is achieved.In order to verify the effectiveness of the proposed algorithm, this method, reference [7] method, and reference [8] method are used for comparative test.


### 5.2. Boundary Positioning Test of Continuous Sports Action

In order to more comprehensively verify the advantages of the proposed algorithm, the following experimental tests compare the continuous sports action boundary positioning of three different algorithms. Taking Figure4 as the test object, different algorithms are used to identify the test object, respectively. The specific experimental comparison results are shown in Figure 5:Figure 4
Test object.Figure 5
Boundary positioning of continuous sports action.By analyzing the boundary location of continuous sports action in Figure5, it can be seen that the proposed algorithm can more accurately identify the test object, and the boundary of continuous sports action is relatively clear, which proves that the proposed algorithm has high recognition accuracy, while the other two algorithms can only identify local features, which need to be improved in the future.


### 5.3. Algorithm Recognition Accuracy Test

In order to verify the recognition accuracy of each algorithm, different experimental environments are set, respectively, and the target number is 20 decomposition actions. The specific experimental results are shown in Figure6.Figure 6
Identification accuracy test.By analyzing the experimental data in Figure6, it can be seen that the recognition accuracy of each algorithm also changes to varying degrees due to different experimental environments. However, compared with the other two algorithms, the proposed algorithm can obtain higher recognition accuracy, and there is only one action error in fuzzy and inclined environment. The main reason is that the proposed algorithm uses CNN model to identify target tags. Even in harsh environments such as abnormal lighting, it can successfully identify images through the iteration of convolution layer and pool layer.


### 5.4. Recall Test

In order to test the accurate recognition performance of continuous sports action based on deep learning algorithm, three methods are used to test the recognition effect of continuous sports action. In the recognition effect test, two groups of 5000 data samples are tested, and three methods are used to test the recognition recall rate. The higher the recall rate, the better the recognition performance of this method. The specific test is shown in Figure7.Figure 7
Recall test of different methods.It can be seen from the analysis of Figure7 that during the test, the recall rate of this method is better than that of literature [7] and literature [8], which shows that the recognition effect of this method is high and verifies the strong recognition performance of this method.


### 5.5. Misidentification Number Test

Because the continuous sports action is greatly affected by the background, it will affect the recognition effect of continuous sports action, and it is easy to have the problems of false recognition and missing recognition. Therefore, in order to verify the recognition effect of different methods, the background noise is set to 50 dB and the interval is 5. Three methods are used to recognize continuous sports actions, respectively. The number of false recognitions, it shows that the method is affected by the background, resulting in poor recognition effect, and vice versa. The test results are shown in Table1.Table 1
Number of misidentifications of three methods.
Background noise (dB)Number of misidentifications by different methods (piece)Paper methodReference [7] methodReference [8] method5121313101215131512171420131714251518153015181735151917401519194516192050161922Through the data in Table1, it is found that the maximum number of false identifications of this method is 16 and the minimum number of false identifications is 12, which shows that the false identification speed of this method increases slowly during the test. During the test period, the maximum number of misidentifications of the method in reference [7] is 19, and the maximum number of misidentifications of the method in reference [8] is 22. It can be seen that the number of misidentifications of the method in reference [8] is higher than that of the method in this paper and reference [7]. At the same time, the jump of the number of misidentifications is large and the rising speed is fast.


## 5.1. Experimental Platform

The server used in the experiment is Intel Xeon (R) CPU e5-2650 v4@2.20 Hz x4s/12 GB geforce gtx1080ti x2 GPU, 64 GB running memory, Ubuntu &amp; 04 operating system and tensorhow0 deep learning framework.After the preparation of the hardware platform, 500 continuous sports action images were collected, and the random gradient descent method was selected to train the CNN algorithm in an end-to-end joint way. Set the learning rate as 0.0087, the attenuation coefficient of the weight as 0.0001, the number of iterations as 2000, save the model once per iteration, and finally select the model with the highest accuracy. According to this process, 1000 training set sample data are used for training, and 2000 iterations are performed on the above training set. Training 500 images takes 24 hours, and the training accuracy loss curve is shown in Figure3.Figure 3
Training loss curve.It can be seen from Figure3 that with the increasing number of iterations, the accuracy loss caused by the training set gradually decreases, indicating that the model training effect is good. The training loss basically converges to a stable value, indicating that the expected training effect is achieved.In order to verify the effectiveness of the proposed algorithm, this method, reference [7] method, and reference [8] method are used for comparative test.


## 5.2. Boundary Positioning Test of Continuous Sports Action

In order to more comprehensively verify the advantages of the proposed algorithm, the following experimental tests compare the continuous sports action boundary positioning of three different algorithms. Taking Figure4 as the test object, different algorithms are used to identify the test object, respectively. The specific experimental comparison results are shown in Figure 5:Figure 4
Test object.Figure 5
Boundary positioning of continuous sports action.By analyzing the boundary location of continuous sports action in Figure5, it can be seen that the proposed algorithm can more accurately identify the test object, and the boundary of continuous sports action is relatively clear, which proves that the proposed algorithm has high recognition accuracy, while the other two algorithms can only identify local features, which need to be improved in the future.


## 5.3. Algorithm Recognition Accuracy Test

In order to verify the recognition accuracy of each algorithm, different experimental environments are set, respectively, and the target number is 20 decomposition actions. The specific experimental results are shown in Figure6.Figure 6
Identification accuracy test.By analyzing the experimental data in Figure6, it can be seen that the recognition accuracy of each algorithm also changes to varying degrees due to different experimental environments. However, compared with the other two algorithms, the proposed algorithm can obtain higher recognition accuracy, and there is only one action error in fuzzy and inclined environment. The main reason is that the proposed algorithm uses CNN model to identify target tags. Even in harsh environments such as abnormal lighting, it can successfully identify images through the iteration of convolution layer and pool layer.


## 5.4. Recall Test

In order to test the accurate recognition performance of continuous sports action based on deep learning algorithm, three methods are used to test the recognition effect of continuous sports action. In the recognition effect test, two groups of 5000 data samples are tested, and three methods are used to test the recognition recall rate. The higher the recall rate, the better the recognition performance of this method. The specific test is shown in Figure7.Figure 7
Recall test of different methods.It can be seen from the analysis of Figure7 that during the test, the recall rate of this method is better than that of literature [7] and literature [8], which shows that the recognition effect of this method is high and verifies the strong recognition performance of this method.


## 5.5. Misidentification Number Test

Because the continuous sports action is greatly affected by the background, it will affect the recognition effect of continuous sports action, and it is easy to have the problems of false recognition and missing recognition. Therefore, in order to verify the recognition effect of different methods, the background noise is set to 50 dB and the interval is 5. Three methods are used to recognize continuous sports actions, respectively. The number of false recognitions, it shows that the method is affected by the background, resulting in poor recognition effect, and vice versa. The test results are shown in Table1.Table 1
Number of misidentifications of three methods.
Background noise (dB)Number of misidentifications by different methods (piece)Paper methodReference [7] methodReference [8] method5121313101215131512171420131714251518153015181735151917401519194516192050161922Through the data in Table1, it is found that the maximum number of false identifications of this method is 16 and the minimum number of false identifications is 12, which shows that the false identification speed of this method increases slowly during the test. During the test period, the maximum number of misidentifications of the method in reference [7] is 19, and the maximum number of misidentifications of the method in reference [8] is 22. It can be seen that the number of misidentifications of the method in reference [8] is higher than that of the method in this paper and reference [7]. At the same time, the jump of the number of misidentifications is large and the rising speed is fast.


## 6. Conclusion

In order to obtain more satisfactory results of continuous sports action recognition and positioning, the deep learning technology is used to realize continuous sports action recognition and positioning. Through the above research, the following conclusions are drawn:(1)
In order to obtain the most accurate continuous sports action positioning target, the target is input into the pretrained deep learning model for recognition, and the target image optimized by deep learning is output(2)
The simulation results show that the proposed algorithm can obtain the number of continuous motion recognition with high accuracy, and the maximum number of false recognition is 16, which has better application performance



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*Source: 3407935-2022-03-29.xml*