# Deep Learning-Based Assessment of Sports-Assisted Teaching and Learning

Authors: Wei Su; Jian Feng
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The current Internet development situation regarding the analysis of sports teaching information is very necessary and can be a way to improve the effectiveness of sports teaching in the information environment. Aiming at the defects of strong subjectivity and low discrimination accuracy of the current sports video classification results, this paper proposes an effective sports video classification method based on deep learning, which can effectively evaluate the sports assisted teaching. Specifically, the key frame features are obtained by using the similarity coefficient key frame extraction algorithm, and the sports video image classification is established through the deep learning coding model. Thus, the ability of the school to rely on the scheme proposed in this paper to improve the teaching facilities, physical education curriculum teaching materials, assessment teaching materials, management, and so on. The results show that for different types of sports videos, the overall effect of the classification of the method in the paper is significantly better than that of other current sports-assisted teaching evaluation methods, which has significantly improved the effect of sports-assisted teaching evaluation.


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## Body

## 1. Introduction

In recent years, with the development of cloud computing and big data technology, new forms of education and teaching such as digital learning, flipped classroom, catechism, and microlesson have emerged, signifying that the field of education is undergoing a revolutionary change [1]. Research on teaching informatization will help us gain a deeper understanding and a comprehensive grasp of the factors that affect teaching effectiveness, grasp the laws of informatized teaching, better apply modern information technology to teaching practice, and promote the development of education informatization. In the field of education, information technology has greatly changed the ecological structure of teaching and learning, and it is rebuilding the education, teaching, and learning process from all aspects that affect the whole education field. It is not only changing the entire educational process but also affecting the relationship between teaching and learning in the teaching process and the value system that determines this relationship [2].The physically active nature of PE teaching and the openness of the teaching space make it more difficult for PE teachers than teachers of other subjects to use IT in PE teaching to improve the effectiveness of their teaching [3]. In PE teaching, the level of information technology is very low, both in terms of the way PE teachers obtain various teaching information and in terms of information exchange between teachers and students and among students. In particular, in order for students to establish correct technical concepts, teachers still need to complete a large number of demonstrations of technical movements [4]. The teacher's age, gender, and physical characteristics, as well as the teacher's own level of comprehension and mastery of technical movements, psychological factors, and other conditions, may affect the effectiveness of the teacher's demonstrations. On the other hand, as PE teaching is mainly a class-based collective teaching, there are bound to be some students who have difficulty in clearly observing the teacher's demonstration, no matter how much they adjust their formation during the demonstration of sports techniques. Therefore, the use of modern highly developed information technology to optimise the way of information exchange and transmission in the process of physical education and to improve the effectiveness of physical education is a subject that needs to be urgently studied in the informatization of physical education [5].One of the key features of intelligent computer-assisted teaching is its suitability for individualised teaching, with Lang being able to select appropriate content and adopt appropriate teaching strategies based on the actual needs of the learners. The system needs to receive timely and accurate feedback from learners during the teaching process and to identify specific teaching strategies based on the feedback model, i.e., the preestablished learning model within the system [6]. Feedback is collected by providing a large number of content-related test questions for learners to answer during the teaching process and then analysing the results to extract useful information. The assessment system consists of three main components: firstly, the creation and maintenance of an open-ended test bank; secondly, the automatic assembly of papers for interactive testing based on the attributes of the test questions selected by the student; and finally, the comprehensive assessment of the learner's mastery of the content based on the test results and the recommendation of the student's next steps in learning based on the assessment results [2]. This is the kind of interactive testing and assessment that assessment systems provide. There are many issues that need to be studied and many mathematical algorithms that need to be analysed in order to design and develop an assessment system. In this paper, we analyse the following aspects of the algorithms.Sports video is an important video resource with the characteristics of a wide range of users. Due to the rapid development of information technology, sports-assisted teaching evaluation research has gradually become a hot issue of concern to relevant personnel [7]. Without an efficient sports video retrieval system, the large amount of sports video information in the network will be cluttered and disorganised. Therefore, how to organise sports video resources with high efficiency and accuracy and to achieve the classification and organisation of sports videos is beneficial in assisting users to efficiently access the sports video content they need [8].In order to overcome the shortcomings of current sports-assisted teaching evaluation methods, a sports-assisted teaching evaluation method based on deep learning is proposed and its performance is analysed through simulation experiments, in anticipation of providing reference values for sports videos and even classification problems in the image field [9].


## 2. Strategies for Improving the Effectiveness of Physical Education

### 2.1. Optimising Physical Education Curriculum Resources

#### 2.1.1. Teaching and Learning Materials for Physical Education Courses

Physical education teaching materials include the teaching content used by teachers and students to complete the teaching tasks and achieve the teaching objectives, including the main teaching and learning objectives, electronic textbooks and teaching reference materials, and multimedia teaching materials (sound, pictures, videos, and teaching software platforms). With the increasingly widespread use of information technology in education, the full use of modern Internet technology and computer technology can effectively integrate all physical education curriculum resources, so as to build a physical education curriculum of teaching materials with the idea of “building blocks” as the core and use modern network technology to achieve interaction and sharing of physical education curriculum building blocks resource base. In the process of PE teaching, PE teachers can conveniently and flexibly call up and produce PE teaching materials suitable for different PE teaching contexts according to the actual needs of PE teaching, the actual situation of different teaching targets, and the corresponding teaching strategies [10].


#### 2.1.2. Physical Education Course Assessment Materials

Assessment materials for PE courses are materials that assess and evaluate students' knowledge, skills, and abilities and include both objective examination questions corresponding to the knowledge of the PE subject and assessment forms for students' behavioural performance in terms of skills and abilities. The assessments are divided into pretests and posttests, which can be used by teachers as part of the teaching or learning materials as required, so that students can complete different assessments of the effectiveness of PE teaching in specific teaching sessions, as required by the teacher's teaching design.


#### 2.1.3. Information on the Management of Physical Education Programmes

The PE curriculum management information is an essential part of the PE curriculum teaching package, providing PE teachers with an overview of the teaching materials and demonstrating how they can be effectively integrated with the different stages of the student’s learning process. It also includes some PE teaching assessment and evaluation materials and important information about the implementation of the curriculum [11]. In addition to providing student guidance templates, the web-based teaching and learning management platform also provides teachers with technical support for course management, including the import of student lists, tracking of student progress, monitoring of learning items, online examinations, grade management, and mechanisms for information exchange between teachers and students or among students.


### 2.2. Optimising the Time Structure of Physical Education

The process of physical education is a dynamic and changing process, and effective physical education in the information technology environment is specifically manifested in the deep integration of information technology and physical education in the process of physical education design [12].Teaching in the information environment is a modern form of teaching performance as opposed to traditional teaching. It attaches importance to the role of modern information technologies, such as modern Internet technology, computer technology, multimedia technology, and telecommunication technology, in teaching, makes full use of modern educational technology means and modern teaching methods, mobilises a variety of teaching media and information resources, and builds a good teaching and learning environment, under the organisation and guidance of teachers. Under the guidance of teachers, the initiative, enthusiasm, and creativity of students are developed completely, so that students can really become active constructors of knowledge and information, thus achieving good teaching results [13]. The main concepts that PE teachers need to change in the information environment are the change of the teaching subject, the change of the teacher's role, and the correct understanding of information technology. The key to the integration of information technology and physical education is not how to use information technology, but to choose teaching media and teaching methods that are more suitable for developing students' abilities according to the needs of physical education at different stages [14].


## 2.1. Optimising Physical Education Curriculum Resources

### 2.1.1. Teaching and Learning Materials for Physical Education Courses

Physical education teaching materials include the teaching content used by teachers and students to complete the teaching tasks and achieve the teaching objectives, including the main teaching and learning objectives, electronic textbooks and teaching reference materials, and multimedia teaching materials (sound, pictures, videos, and teaching software platforms). With the increasingly widespread use of information technology in education, the full use of modern Internet technology and computer technology can effectively integrate all physical education curriculum resources, so as to build a physical education curriculum of teaching materials with the idea of “building blocks” as the core and use modern network technology to achieve interaction and sharing of physical education curriculum building blocks resource base. In the process of PE teaching, PE teachers can conveniently and flexibly call up and produce PE teaching materials suitable for different PE teaching contexts according to the actual needs of PE teaching, the actual situation of different teaching targets, and the corresponding teaching strategies [10].


### 2.1.2. Physical Education Course Assessment Materials

Assessment materials for PE courses are materials that assess and evaluate students' knowledge, skills, and abilities and include both objective examination questions corresponding to the knowledge of the PE subject and assessment forms for students' behavioural performance in terms of skills and abilities. The assessments are divided into pretests and posttests, which can be used by teachers as part of the teaching or learning materials as required, so that students can complete different assessments of the effectiveness of PE teaching in specific teaching sessions, as required by the teacher's teaching design.


### 2.1.3. Information on the Management of Physical Education Programmes

The PE curriculum management information is an essential part of the PE curriculum teaching package, providing PE teachers with an overview of the teaching materials and demonstrating how they can be effectively integrated with the different stages of the student’s learning process. It also includes some PE teaching assessment and evaluation materials and important information about the implementation of the curriculum [11]. In addition to providing student guidance templates, the web-based teaching and learning management platform also provides teachers with technical support for course management, including the import of student lists, tracking of student progress, monitoring of learning items, online examinations, grade management, and mechanisms for information exchange between teachers and students or among students.


## 2.1.1. Teaching and Learning Materials for Physical Education Courses

Physical education teaching materials include the teaching content used by teachers and students to complete the teaching tasks and achieve the teaching objectives, including the main teaching and learning objectives, electronic textbooks and teaching reference materials, and multimedia teaching materials (sound, pictures, videos, and teaching software platforms). With the increasingly widespread use of information technology in education, the full use of modern Internet technology and computer technology can effectively integrate all physical education curriculum resources, so as to build a physical education curriculum of teaching materials with the idea of “building blocks” as the core and use modern network technology to achieve interaction and sharing of physical education curriculum building blocks resource base. In the process of PE teaching, PE teachers can conveniently and flexibly call up and produce PE teaching materials suitable for different PE teaching contexts according to the actual needs of PE teaching, the actual situation of different teaching targets, and the corresponding teaching strategies [10].


## 2.1.2. Physical Education Course Assessment Materials

Assessment materials for PE courses are materials that assess and evaluate students' knowledge, skills, and abilities and include both objective examination questions corresponding to the knowledge of the PE subject and assessment forms for students' behavioural performance in terms of skills and abilities. The assessments are divided into pretests and posttests, which can be used by teachers as part of the teaching or learning materials as required, so that students can complete different assessments of the effectiveness of PE teaching in specific teaching sessions, as required by the teacher's teaching design.


## 2.1.3. Information on the Management of Physical Education Programmes

The PE curriculum management information is an essential part of the PE curriculum teaching package, providing PE teachers with an overview of the teaching materials and demonstrating how they can be effectively integrated with the different stages of the student’s learning process. It also includes some PE teaching assessment and evaluation materials and important information about the implementation of the curriculum [11]. In addition to providing student guidance templates, the web-based teaching and learning management platform also provides teachers with technical support for course management, including the import of student lists, tracking of student progress, monitoring of learning items, online examinations, grade management, and mechanisms for information exchange between teachers and students or among students.


## 2.2. Optimising the Time Structure of Physical Education

The process of physical education is a dynamic and changing process, and effective physical education in the information technology environment is specifically manifested in the deep integration of information technology and physical education in the process of physical education design [12].Teaching in the information environment is a modern form of teaching performance as opposed to traditional teaching. It attaches importance to the role of modern information technologies, such as modern Internet technology, computer technology, multimedia technology, and telecommunication technology, in teaching, makes full use of modern educational technology means and modern teaching methods, mobilises a variety of teaching media and information resources, and builds a good teaching and learning environment, under the organisation and guidance of teachers. Under the guidance of teachers, the initiative, enthusiasm, and creativity of students are developed completely, so that students can really become active constructors of knowledge and information, thus achieving good teaching results [13]. The main concepts that PE teachers need to change in the information environment are the change of the teaching subject, the change of the teacher's role, and the correct understanding of information technology. The key to the integration of information technology and physical education is not how to use information technology, but to choose teaching media and teaching methods that are more suitable for developing students' abilities according to the needs of physical education at different stages [14].


## 3. Difficulty Algorithm for Sports Test Questions

At present, there are generally three methods for selecting topics in the test question bank. One is to allow users to input the required test question types and chapters and directly use random functions to randomly select test questions to form test papers. Second, use random function to select questions within the range proposed by users, so that the selected questions can indeed meet the requirements of users, but it is too cumbersome and workload for users. Third, the user will display or print all the questions in the question bank, and then manually (expert) select the questions to form the test paper, or after the user is familiar with the contents and parameters of the question bank, understand the distribution of the questions. This way of selecting questions can make the questions of the test paper more accurate. This method of question selection can be more accurate while selecting papers that meet the requirements, but this method is also too cumbersome and demanding for the user, making it difficult to reflect the advantages of a test bank. To address these problems, a mathematical model of question selection was established using the binomial distribution functionBn,p of a discrete random variable to determine the distribution of question type and difficulty and then a random function was used to select questions, with good results.Since the concept of a randomly drawn test question does not depend on the results of other drawn questions, there are only two possibilities for each test question, i.e., to be drawn or not to be drawn, and it is random in nature. The randomly drawn question event can therefore be considered to conform to the binomial distribution function of a discrete random variableBn,p, i.e.,(1)Pnk=nkpkqn−k=nkpk1−pn−k,where k = 1, 2, …, n, n is a positive integer, 1 > P > 0, q > 0, and p + q = 1; the mean of the binomial distribution is(2)Q=np.In the model,k denotes the difficulty level, Pn(k) denotes the probability that the difficulty level is k (i.e., the proportion of questions with difficulty level k in the total number of questions), and Q denotes the average difficulty of the test paper. Because for n, P fixed binomial distribution B(n, p), when k increases, the probability P{x = k} first monotonically increases to the maximum and then monotonically decreases and the probability of both ends is very small that it can be ignored, so in the actual calculation, take n = 6 a total of 7 levels of difficulty, from formula (2) to find p, p, n, k into formula (1), you can find out each difficulty. The proportion of Pn(k) in the total number of questions for each difficulty level can be found by substituting p, n, and k into (1) and then multiplying Pn(k) by the total number of questions to obtain the number of questions that should be taken for each difficulty level.The binomial functionB(n, p) of discrete random variables is used to establish the mathematical model of random question selection, which can well solve the problem of difficulty distribution in the process of test bank preparation. On this basis, the random function is then used to randomly select questions within this difficulty distribution, with good randomness, and if the question type, chapter range, and other conditions are added, satisfactory questions can be automatically selected.


## 4. Evaluating Physical Education Assistance

### 4.1. Similarity Coefficient Key Frame Extraction Algorithm Based on Lens Boundaries

#### 4.1.1. Sports Video Feature Extraction

Features represent a target and certain attributes that can be quantified. In the case of sports videos, these include mainly generic features and domain-specific features. Considering the efficiency of key frame extraction for sports videos, the sports video image features are set into colour histograms and colour division descriptors [15]. In general, the description of sports video image colour belongs to the colour space problem and the key frame extraction algorithm based on the similarity coefficients of the lens boundaries used in this paper; after the image is in the HSV colour space derived from the colour histogram, the histogram index is first initialised and the similarity of the two frames ga and gb of the sports video histogram Histah,s,v can be seen as follows:(3)simga,gb=∑h=015∑s=03∑v=03minHistah,s,v,Histbh,s,v,where 0 describes a very large difference between the colour histograms of the two images and 1 describes the same difference between the colour histograms of the two images. For the colour segment descriptor, which is used to represent the spatial part of the colour in the sports video image, the feature extraction process is as follows: the sports video image is chunked; the dominant colour is selected; the Y, Cb, and Cr components of the 64 pixels are discrete cosine-transformed to obtain three sets of coefficients; finally, the obtained discrete cosine coefficients are Zigzag scanned, and a small number of low-frequency coefficients are selected to create a new segment descriptor [16]. The distance between frames ga and gb is then set to Ay, ACd, and ACR.(4)Dist=∑h=164ϖYhAYjh−AYjh2+∑h=164ϖCbhACbjh−ACbjh2+∑h=164ϖCrhACrjh−ACrjh2,where AYjh describes the hth term of the discrete cosine coefficient of the Y component of frame ga and ϖ describes the weight.


#### 4.1.2. Sports Video Boundary Detection

The shot boundary coefficients are set according to the properties of the sports video domain transformation [6]. Assuming that the width of the domain window is 2M + 1, the neighbourhood window frame difference for frame a is as follows:(5)Aswa=∑b=1MM−b+1HAa−b,a+b,where H=HM+1/2.The variation in shots is higher than the variation between shots; if the distance between two random frames in a shot isAs and the distance between shots is Ab, then As < Ab. If at this point M = 3 and there is a shot mutation between frame H and frame H + 1, then the sequence of constants Usw (b) that can be obtained is (1, 3, 6, 6, 3, 1). The similarity coefficient of the shot boundary at frame a can be set as follows:(6)Bsbsa=∑b=−M+1MAswa+bUswM+L∑b=−M=1MAswa+b2∑b=−M=1MUswM+b2.Here, if the similarity coefficient of the shot boundaries is close to 1 when transforming between adjacent frames, there are very small values between 0 and 1 in the remaining conditions.


#### 4.1.3. Key Frame Sequence Clustering

In order to reduce the iterative nature of the final key frame sequence, the key frame sequences were clustered by K-means clustering, with the final K-value set according to the cluster validity method [17], because the same footage in the same sports video can occur repeatedly, resulting in repeated key frame sequences being obtained.The clustering performance metrics are(7)G=xScata+disa,where G describes the clustering result of sports video key frame sequences; Scat(a) and dis(a) both describe the key frame sequence classes; and x and y describe the interclass distances in turn.Since these two values vary widely, a weighting factor dis(dmax) is set, dmax indicating the maximum predetermined number of clusters. The d obtained when this value is at the minimum is the optimal number of clusters [16].


### 4.2. Sports Video Image Classification Based on Deep Learning Coding Models

A multilayer restricted Boltzmann machine is used to encode and learnG into a visual lexicon with representational properties. Based on the spatial information of G, the neighbouring G features are set as the input to the RBM and the RBM is trained using the CD fast algorithm to obtain the hidden layer features; afterwards, the neighbouring hidden layer features are set as the input to the lower RBM to obtain the output dictionary [18, 19]. When ϖ1 and ϖ2 are the connection weights of the RBM, there is one explicit layer and one hidden layer of the RBM and the neurons at the same level in the RBM are not connected based on the connection relationship [18]. When the network is trained, the connection between the hidden and explicit layers of the RBM is achieved according to a conditional chance distribution, where the conditional chance between the explicit and hidden layers is(8)qsi|y=sigmoidci+∑j=1iϖjiyj,qyj|s=sigmoidbi+∑j=1iϖjisj,where yj and si describe the feature and coding layers of the sports video, i.e., the explicit and implicit layers in the RBM, respectively.By setting the weight matrixϖ and the hidden layer bias vector c, the input layer features y can be encoded into a visual dictionary s. Correspondingly, by setting ϖ and the explicit layer bias matrix b, the sports video features can be reconstructed from the visual dictionaries. For a set of input and encoding layers in the RBM, its energy function is(9)Dy,s=−lgqy,s=−∑j=1i∑i=1iyjϖjisi−∑i=1ibjyj−∑i=1icisi.We calculate the energy function to be able to obtain the joint chance distribution function of (y, s).(10)qy,s=e−Dy,s∑y,se−Dy,s,where e describes the derivation factor.To obtain the edge distribution of the joint sports video distribution, the chance distribution of the feature input nodes is(11)qy=∑y,se−Dy,s∑y,se−Dy,s.The RBM network is trained primarily to maximizeq(y), and its gradient is(12)∂lgqy∂ϖji=yjsidata−yjsimodel,where yjsidata describes the expected value of the prior chance distribution in the sports video training dataset and yjsimodel describes the expected value of the chance distribution in this model.In general, it is possible to obtain sample models using a Monte Carlo Markov chain approach.(13)yj=gdecs,ϖj=μ∑i=0iϖjisi,where gdec describes the sports video key frame feature vector.The CD algorithm is used to implement fast learning into the RBM to improve the convergence efficiency of the parameters, and the amount of updates to obtain the weights isϖji.(14)Δϖji=φyjsidata−yjsimodel,where φ describes the speed of learning.The CD algorithm is able to acquire the latest parameters of the sports video features until the parameters converge and the initial visual dictionary is acquired.


## 4.1. Similarity Coefficient Key Frame Extraction Algorithm Based on Lens Boundaries

### 4.1.1. Sports Video Feature Extraction

Features represent a target and certain attributes that can be quantified. In the case of sports videos, these include mainly generic features and domain-specific features. Considering the efficiency of key frame extraction for sports videos, the sports video image features are set into colour histograms and colour division descriptors [15]. In general, the description of sports video image colour belongs to the colour space problem and the key frame extraction algorithm based on the similarity coefficients of the lens boundaries used in this paper; after the image is in the HSV colour space derived from the colour histogram, the histogram index is first initialised and the similarity of the two frames ga and gb of the sports video histogram Histah,s,v can be seen as follows:(3)simga,gb=∑h=015∑s=03∑v=03minHistah,s,v,Histbh,s,v,where 0 describes a very large difference between the colour histograms of the two images and 1 describes the same difference between the colour histograms of the two images. For the colour segment descriptor, which is used to represent the spatial part of the colour in the sports video image, the feature extraction process is as follows: the sports video image is chunked; the dominant colour is selected; the Y, Cb, and Cr components of the 64 pixels are discrete cosine-transformed to obtain three sets of coefficients; finally, the obtained discrete cosine coefficients are Zigzag scanned, and a small number of low-frequency coefficients are selected to create a new segment descriptor [16]. The distance between frames ga and gb is then set to Ay, ACd, and ACR.(4)Dist=∑h=164ϖYhAYjh−AYjh2+∑h=164ϖCbhACbjh−ACbjh2+∑h=164ϖCrhACrjh−ACrjh2,where AYjh describes the hth term of the discrete cosine coefficient of the Y component of frame ga and ϖ describes the weight.


### 4.1.2. Sports Video Boundary Detection

The shot boundary coefficients are set according to the properties of the sports video domain transformation [6]. Assuming that the width of the domain window is 2M + 1, the neighbourhood window frame difference for frame a is as follows:(5)Aswa=∑b=1MM−b+1HAa−b,a+b,where H=HM+1/2.The variation in shots is higher than the variation between shots; if the distance between two random frames in a shot isAs and the distance between shots is Ab, then As < Ab. If at this point M = 3 and there is a shot mutation between frame H and frame H + 1, then the sequence of constants Usw (b) that can be obtained is (1, 3, 6, 6, 3, 1). The similarity coefficient of the shot boundary at frame a can be set as follows:(6)Bsbsa=∑b=−M+1MAswa+bUswM+L∑b=−M=1MAswa+b2∑b=−M=1MUswM+b2.Here, if the similarity coefficient of the shot boundaries is close to 1 when transforming between adjacent frames, there are very small values between 0 and 1 in the remaining conditions.


### 4.1.3. Key Frame Sequence Clustering

In order to reduce the iterative nature of the final key frame sequence, the key frame sequences were clustered by K-means clustering, with the final K-value set according to the cluster validity method [17], because the same footage in the same sports video can occur repeatedly, resulting in repeated key frame sequences being obtained.The clustering performance metrics are(7)G=xScata+disa,where G describes the clustering result of sports video key frame sequences; Scat(a) and dis(a) both describe the key frame sequence classes; and x and y describe the interclass distances in turn.Since these two values vary widely, a weighting factor dis(dmax) is set, dmax indicating the maximum predetermined number of clusters. The d obtained when this value is at the minimum is the optimal number of clusters [16].


## 4.1.1. Sports Video Feature Extraction

Features represent a target and certain attributes that can be quantified. In the case of sports videos, these include mainly generic features and domain-specific features. Considering the efficiency of key frame extraction for sports videos, the sports video image features are set into colour histograms and colour division descriptors [15]. In general, the description of sports video image colour belongs to the colour space problem and the key frame extraction algorithm based on the similarity coefficients of the lens boundaries used in this paper; after the image is in the HSV colour space derived from the colour histogram, the histogram index is first initialised and the similarity of the two frames ga and gb of the sports video histogram Histah,s,v can be seen as follows:(3)simga,gb=∑h=015∑s=03∑v=03minHistah,s,v,Histbh,s,v,where 0 describes a very large difference between the colour histograms of the two images and 1 describes the same difference between the colour histograms of the two images. For the colour segment descriptor, which is used to represent the spatial part of the colour in the sports video image, the feature extraction process is as follows: the sports video image is chunked; the dominant colour is selected; the Y, Cb, and Cr components of the 64 pixels are discrete cosine-transformed to obtain three sets of coefficients; finally, the obtained discrete cosine coefficients are Zigzag scanned, and a small number of low-frequency coefficients are selected to create a new segment descriptor [16]. The distance between frames ga and gb is then set to Ay, ACd, and ACR.(4)Dist=∑h=164ϖYhAYjh−AYjh2+∑h=164ϖCbhACbjh−ACbjh2+∑h=164ϖCrhACrjh−ACrjh2,where AYjh describes the hth term of the discrete cosine coefficient of the Y component of frame ga and ϖ describes the weight.


## 4.1.2. Sports Video Boundary Detection

The shot boundary coefficients are set according to the properties of the sports video domain transformation [6]. Assuming that the width of the domain window is 2M + 1, the neighbourhood window frame difference for frame a is as follows:(5)Aswa=∑b=1MM−b+1HAa−b,a+b,where H=HM+1/2.The variation in shots is higher than the variation between shots; if the distance between two random frames in a shot isAs and the distance between shots is Ab, then As < Ab. If at this point M = 3 and there is a shot mutation between frame H and frame H + 1, then the sequence of constants Usw (b) that can be obtained is (1, 3, 6, 6, 3, 1). The similarity coefficient of the shot boundary at frame a can be set as follows:(6)Bsbsa=∑b=−M+1MAswa+bUswM+L∑b=−M=1MAswa+b2∑b=−M=1MUswM+b2.Here, if the similarity coefficient of the shot boundaries is close to 1 when transforming between adjacent frames, there are very small values between 0 and 1 in the remaining conditions.


## 4.1.3. Key Frame Sequence Clustering

In order to reduce the iterative nature of the final key frame sequence, the key frame sequences were clustered by K-means clustering, with the final K-value set according to the cluster validity method [17], because the same footage in the same sports video can occur repeatedly, resulting in repeated key frame sequences being obtained.The clustering performance metrics are(7)G=xScata+disa,where G describes the clustering result of sports video key frame sequences; Scat(a) and dis(a) both describe the key frame sequence classes; and x and y describe the interclass distances in turn.Since these two values vary widely, a weighting factor dis(dmax) is set, dmax indicating the maximum predetermined number of clusters. The d obtained when this value is at the minimum is the optimal number of clusters [16].


## 4.2. Sports Video Image Classification Based on Deep Learning Coding Models

A multilayer restricted Boltzmann machine is used to encode and learnG into a visual lexicon with representational properties. Based on the spatial information of G, the neighbouring G features are set as the input to the RBM and the RBM is trained using the CD fast algorithm to obtain the hidden layer features; afterwards, the neighbouring hidden layer features are set as the input to the lower RBM to obtain the output dictionary [18, 19]. When ϖ1 and ϖ2 are the connection weights of the RBM, there is one explicit layer and one hidden layer of the RBM and the neurons at the same level in the RBM are not connected based on the connection relationship [18]. When the network is trained, the connection between the hidden and explicit layers of the RBM is achieved according to a conditional chance distribution, where the conditional chance between the explicit and hidden layers is(8)qsi|y=sigmoidci+∑j=1iϖjiyj,qyj|s=sigmoidbi+∑j=1iϖjisj,where yj and si describe the feature and coding layers of the sports video, i.e., the explicit and implicit layers in the RBM, respectively.By setting the weight matrixϖ and the hidden layer bias vector c, the input layer features y can be encoded into a visual dictionary s. Correspondingly, by setting ϖ and the explicit layer bias matrix b, the sports video features can be reconstructed from the visual dictionaries. For a set of input and encoding layers in the RBM, its energy function is(9)Dy,s=−lgqy,s=−∑j=1i∑i=1iyjϖjisi−∑i=1ibjyj−∑i=1icisi.We calculate the energy function to be able to obtain the joint chance distribution function of (y, s).(10)qy,s=e−Dy,s∑y,se−Dy,s,where e describes the derivation factor.To obtain the edge distribution of the joint sports video distribution, the chance distribution of the feature input nodes is(11)qy=∑y,se−Dy,s∑y,se−Dy,s.The RBM network is trained primarily to maximizeq(y), and its gradient is(12)∂lgqy∂ϖji=yjsidata−yjsimodel,where yjsidata describes the expected value of the prior chance distribution in the sports video training dataset and yjsimodel describes the expected value of the chance distribution in this model.In general, it is possible to obtain sample models using a Monte Carlo Markov chain approach.(13)yj=gdecs,ϖj=μ∑i=0iϖjisi,where gdec describes the sports video key frame feature vector.The CD algorithm is used to implement fast learning into the RBM to improve the convergence efficiency of the parameters, and the amount of updates to obtain the weights isϖji.(14)Δϖji=φyjsidata−yjsimodel,where φ describes the speed of learning.The CD algorithm is able to acquire the latest parameters of the sports video features until the parameters converge and the initial visual dictionary is acquired.


## 5. Simulation Experiments

### 5.1. Experimental Subjects

In order to analyse the effectiveness of the classification method in this paper, three categories of sports videos were set: figure skating, badminton, and yoga.


### 5.2. Experimental Results and Analysis

The results are shown in Table1, which tests the comprehensiveness of the extracted key frames of the three sports video categories: figure skating, badminton, and yoga. Table 1 shows that the average missing rate of key frame extraction is less than 0.02 for figure skating, badminton, and yoga, which indicates that the method can fully extract key frame features in sports video images.Table 1
Key frame missing rate of sports video images for the method in the paper.
Extraction timesFigure skatingBadmintonYoga10.020.020.0120.020.020.0130.010.020.0140.010.020.0150.010.030.0260.020.030.0270.010.020.03Mean value0.010.020.02The results are shown in Figure1, which shows that the classification accuracy of the method increases with the size of the visual dictionary of sports videos and stabilizes at 0.98 when the size of the visual dictionary of sports videos reaches 2000 MB.Figure 1
Interference of differential visual measurement point size on the method of this paper.In order to test the effect of supervised fine-tuning on the classification of the in-text method, the effect of the in-text method PET assessment with and without fine-tuning is shown in Figure2. As can be seen from Figure 2, there is a significant difference between the effect of supervised fine-tuning on in-text method classification and unsupervised fine-tuning, with supervised fine-tuning improving in-text method classification accuracy. This is due to the fact that supervised fine-tuning can adjust the parameters of each layer of the deep learning network in the form of error backpropagation to optimise the classification effect.Figure 2
The role of fine-tuning on the effect of method classification in the text.


### 5.3. Teaching Aid Effectiveness

The information-based teaching environment should create the conditions for the creation of a modern physical education learning process or a new physical education teaching model.We create conditions for the management and evaluation of modern physical education learning resources and information. We apply modern scientific theories and technological achievements to establish school teaching information management systems to realise the efficient collection, processing, and presentation of a wide variety of teaching information in the process of physical education teaching and make full use of modern media technology to strengthen teaching activities, such as establishing physical education teaching television monitoring, computer teaching management, school or faculty teaching management, learning resource retrieval and management, physical education teaching information feedback, and physical education teaching effect. The modern physical education teaching information resource management system with functions of assessment and evaluation is shown in Figure3.Figure 3
Effectiveness of different physical education systems.The informative teaching content can be transmitted and shared over long distances through the network, and learners can access the information resources they need by networking through computers and mobile terminals. The linear organisation of hypermedia is as follows: under the informatization environment, the teaching content is constructed using hypermedia technology, supporting multimedia information such as text, audio, video, image, and animation and using the hypertext way of organising and managing information nonlinearly in a mesh structure to effectively organise teaching information, which is suitable for the human brain's cognitive way of thinking and is also conducive to effectively organising teaching information and promoting the transfer of knowledge and skills.


## 5.1. Experimental Subjects

In order to analyse the effectiveness of the classification method in this paper, three categories of sports videos were set: figure skating, badminton, and yoga.


## 5.2. Experimental Results and Analysis

The results are shown in Table1, which tests the comprehensiveness of the extracted key frames of the three sports video categories: figure skating, badminton, and yoga. Table 1 shows that the average missing rate of key frame extraction is less than 0.02 for figure skating, badminton, and yoga, which indicates that the method can fully extract key frame features in sports video images.Table 1
Key frame missing rate of sports video images for the method in the paper.
Extraction timesFigure skatingBadmintonYoga10.020.020.0120.020.020.0130.010.020.0140.010.020.0150.010.030.0260.020.030.0270.010.020.03Mean value0.010.020.02The results are shown in Figure1, which shows that the classification accuracy of the method increases with the size of the visual dictionary of sports videos and stabilizes at 0.98 when the size of the visual dictionary of sports videos reaches 2000 MB.Figure 1
Interference of differential visual measurement point size on the method of this paper.In order to test the effect of supervised fine-tuning on the classification of the in-text method, the effect of the in-text method PET assessment with and without fine-tuning is shown in Figure2. As can be seen from Figure 2, there is a significant difference between the effect of supervised fine-tuning on in-text method classification and unsupervised fine-tuning, with supervised fine-tuning improving in-text method classification accuracy. This is due to the fact that supervised fine-tuning can adjust the parameters of each layer of the deep learning network in the form of error backpropagation to optimise the classification effect.Figure 2
The role of fine-tuning on the effect of method classification in the text.


## 5.3. Teaching Aid Effectiveness

The information-based teaching environment should create the conditions for the creation of a modern physical education learning process or a new physical education teaching model.We create conditions for the management and evaluation of modern physical education learning resources and information. We apply modern scientific theories and technological achievements to establish school teaching information management systems to realise the efficient collection, processing, and presentation of a wide variety of teaching information in the process of physical education teaching and make full use of modern media technology to strengthen teaching activities, such as establishing physical education teaching television monitoring, computer teaching management, school or faculty teaching management, learning resource retrieval and management, physical education teaching information feedback, and physical education teaching effect. The modern physical education teaching information resource management system with functions of assessment and evaluation is shown in Figure3.Figure 3
Effectiveness of different physical education systems.The informative teaching content can be transmitted and shared over long distances through the network, and learners can access the information resources they need by networking through computers and mobile terminals. The linear organisation of hypermedia is as follows: under the informatization environment, the teaching content is constructed using hypermedia technology, supporting multimedia information such as text, audio, video, image, and animation and using the hypertext way of organising and managing information nonlinearly in a mesh structure to effectively organise teaching information, which is suitable for the human brain's cognitive way of thinking and is also conducive to effectively organising teaching information and promoting the transfer of knowledge and skills.


## 6. Conclusions

With the continuous development of information technology and the emergence of new technologies and media, information technology has become an important direction for the development and reform of physical education. In this environment, physical education teachers should firstly establish a modern concept of physical education and consciously use the theories and methods of modern information technology to improve the effectiveness of physical education. Secondly, they should think about the objectives of physical education teaching, teaching situations, teaching strategies, and teaching evaluation in the informationized environment from different levels and actively use modern information technology to create an informationized physical education teaching environment. The experimental results show that for three types of sports videos, namely, figure skating, badminton, and yoga, the classification accuracy, recall, and the maximum value of F1 of the method in the paper are better than those of the comparison method and a better evaluation result of sports-assisted teaching is obtained.



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*Source: 7833292-2022-04-27.xml*