# Feature Extraction of Foul Action of Football Players Based on Machine Vision

Authors: Hao Guan; Hualiang Niu
Journal: Mobile Information Systems
 (2022)

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## Abstract

With the improvement of technology and tactics, the rhythm of football match is getting faster and faster, which leads to more intense competition behavior in a football match; the physical contact of both players is also increasing, and the frequency of fouls by football players is getting higher and higher. This leads to fouls by players. Because of the error of visual analysis, in the crowd of high-level football players, the traditional football players’ foul behavior feature extraction method has the problem of low precision of foul action feature extraction. This paper mainly studies the feature extraction of soccer players’ foul action based on machine vision. To solve these problems, this paper uses a machine vision-based football player foul action feature extraction method, using a machine vision system to obtain football player action image, based on threshold recognition algorithm to identify the football player’s foul action. Based on the recognition of the foul action image, the potential function sequence of the foul action sequence is established by the Harris 3D operator, and the characteristic data of football player foul action are filtered by the AdaBoost algorithm. The simulation results show that this method has high accuracy in identifying fouls in the range of high-level football players and effectively reduces the recognition error. The method proposed in this paper can effectively analyze the characteristics of foul action and help football clubs to develop more perfect tactics.


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## Body

## 1. Introduction

In the process of frequent fouls in football matches, in order to ensure the safety of football players and make them play at the highest level, FIFA constantly modifies and consummates the rules of football matches. The increase of football match intensity brings great challenges to the judgment of referees.The study of feature extraction of foul action of football players based on machine vision has attracted the interest of many experts and has been studied by many teams. For example, some teams found that in the analysis of the characteristics of direct free kick foul in the final stage of the 17th World Cup, the land run analyzed the foul position and regional characteristics of direct free-kick, and considered that the fouls in contemporary football matches were mainly direct free-kick fouls, all of which were dominated by lower limb fouls, in which fouls were mostly tripped, followed by push, pull, and kick. Direct free-kick fouls are the most common in midfield, especially near midfield, in front of the back, and in the front, fouls are more frequent [1]. A comparative study of the foul characteristics of Zuo Haochang in the 2010 and 2006 World Cup finals shows that the number of fouls in the 2006 World Cup team is significantly higher than that in the 2010 World Cup. By comparing and analyzing the total number of fouls in the last three World Cup teams, the number of fouls is reduced year by year. The number of fouls in the game is obviously related to the final result, and the team with good fouls is less than the team with poor fouls [2, 3]. By analyzing the 2004 European Cup players’ fouls, some teams found that the 2004 European Cup foul exceeded the first half in the second half of the year because of the lack of physical reserves in the second half of the game and the score gap. According to the statistics of fouls, European players have more fouls on upper limbs than lower limbs [4]. A statistical study of the foul characteristics of players in the 2004 Asian Cup and the European Cup of the same year, found that in today’s football match, players in Europe and Asia were more active in terms of foul motivation. There are worse fouls and offensive fouls [5]. During the first half and the second half of the game, there was a high incidence of fouls. The study found that in the above two periods, the player’s mentality changed a lot. In the second half of the second half, due to the decline in physical fitness and the gap in the field score, it was easier to foul [6]. Common features of the two cup games are that the second half has more red and yellow cards than the first half. The penalty area is characterized by the largest number of fouls in the Eurasian midfield, a similar number of fouls by the forward players, more fouls in the backcourt of the Asian Cup, more fouls in the frontcourt of the European Cup. Finally, European fouls on upper limbs exceed lower limbs, most of which are push and tripping. Asian fouls on lower limbs are more than upper limbs, focusing on pulling fouls and kicking [7]. Although their research results are very rich, there are still some shortcomings.The purpose of this paper is to understand the foul characteristics of male and female players in high-level football matches in the world and to provide a theoretical reference for our male and female athletes and coaches to learn about the foul characteristics of powerful teams in the world. At the same time, it provides a theoretical reference for men and women referees at all levels to understand and study the foul characteristics of high level and strong teams in the world, to improve the professional level of referees at all levels, to pay attention to the differences between men and women in the future competition law enforcement, to ensure the smooth progress of the competition, and to promote the normal play of the skills and tactics of both athletes.


## 2. Method

### 2.1. Foul Action Recognition

Based on the action image of the football player, the threshold recognition algorithm is used to identify the foul behavior of the football player, and to prepare for the establishment of the sequence potential function of the foul action. The more complicated situation will appear in a football match, which leads to the complex background of the player’s action picture. Therefore, the threshold recognition algorithm is used to identify the foul action. It is assumed that the number of effective pixels in the action image is ne, the action image is a matrix, and the coordinates of four points areA (x1, y1), b (x2, y2), c (x3, y3), and d (x4, y4). The formulas for calculating the parameters P and S are shown below [8].(1)P=x2−x1y3−y1,(2)S=x2−x1∗y3−y1.


### 2.2. Establishment of the Preorder Potential Function for Foul Action

Using foul action to identify results, the Harris 3D operator establishes a sequence potential function for foul action, provides support for feature extraction of foul action. Harris3D operator is used to mine foul movements, extract the temporal and spatial interest points for each different foul action in the image, the features of gradient histogram and towline direct graph are obtained, divided into 72 and 90 dimensions, fusion gradient histogram, and flow direction histogram, get a 162-dimensional eigenvector, this is a potential feature of foul behavior. The key point of choosing a football player, e.g. (xzi, yzi), according to the structural characteristics of the human body, the body of a football player is divided into seven local reference points: shoulder, left and right arm, left and right leg, left and right foot, for example (ai, bi), n represents a local reference center, it is called a point of interest, seven regions based on local reference points; (xj) and (yj) represents each point of interest in time and space [9].(3)n=argminai-xj2+bi-yj2xzi,yzi.


### 2.3. Feature Extraction of Foul Action

On the basis of the sequence potential function, the feature data of football players’ foul behavior are screened by enhancement algorithm, and the feature extraction of training and identifying football players’ foul behavior is realized. Suppose football player fouls, the action image training sample set is (x1, y1)…(xi, yi)…(xn, yn), where xi denotes foul action image sample data, yi denotes foul action image sample mark. The sample data of any foul action in the training sample N are used as known samples. The AdaBoost algorithm is a weak classifier, and the training sample is obtained. Suppose, based on φ(y, h, x), the sample error rate of the football player's foul action image is calculated, and formula (4) is obtained, where, εt represents the sample error rate [10].(4)εt=ϕY,h,X×htxi≠yix1,y1,⋯xi,yi,⋯xN,yN.According to the calculation results of formula (4), the features of the extracted foul action are marked as 1, and the rest are marked as −1. By the iterative method, the feature extraction model of football player's foul behavior is established. The representation of the model is as follows [11]:(5)C^i=argmindi−Cdj2∗εt.Thedi represents the i feature data in the foul action sequence, and the C (dj) represents the category of the foul action feature data in the training sample. According to formula (5), the feature extraction of the behavior of football players based on machine vision is realized.


### 2.4. Establishment of Membership Matrix

Clustering bottom foul action features usingK-mean, K, for cluster centers get K × 162-dimension foul action image, at the same time generate three levels and seven areas of foul action images, each area foul action T frame is defined as the space-time foul action module, represents the characteristics of a part of a football player’s foul behavior. When the foul action image unit of two overlapping T/2 frames are connected, all the action units in each foul action image are organically synthesized, a series of temporal and spatial foul action units with length, after k-mean clustering, the characteristics of the echo bow of the action-image measured by the foul action data set are obtained as formula (6), where fp represents the BOW features of foul action image p echo, Kn indicates the number of cluster centers in the foul range, and N represents the sequence length of the temporal and spatial units of the foul action image [12].(6)fp=Kn×NK∗K×162P.The fusion formula is BOW, whereFp represents the fusion characteristics of each region at the corresponding level.(7)FP=∑n∈1,7Kn×N.


## 2.1. Foul Action Recognition

Based on the action image of the football player, the threshold recognition algorithm is used to identify the foul behavior of the football player, and to prepare for the establishment of the sequence potential function of the foul action. The more complicated situation will appear in a football match, which leads to the complex background of the player’s action picture. Therefore, the threshold recognition algorithm is used to identify the foul action. It is assumed that the number of effective pixels in the action image is ne, the action image is a matrix, and the coordinates of four points areA (x1, y1), b (x2, y2), c (x3, y3), and d (x4, y4). The formulas for calculating the parameters P and S are shown below [8].(1)P=x2−x1y3−y1,(2)S=x2−x1∗y3−y1.


## 2.2. Establishment of the Preorder Potential Function for Foul Action

Using foul action to identify results, the Harris 3D operator establishes a sequence potential function for foul action, provides support for feature extraction of foul action. Harris3D operator is used to mine foul movements, extract the temporal and spatial interest points for each different foul action in the image, the features of gradient histogram and towline direct graph are obtained, divided into 72 and 90 dimensions, fusion gradient histogram, and flow direction histogram, get a 162-dimensional eigenvector, this is a potential feature of foul behavior. The key point of choosing a football player, e.g. (xzi, yzi), according to the structural characteristics of the human body, the body of a football player is divided into seven local reference points: shoulder, left and right arm, left and right leg, left and right foot, for example (ai, bi), n represents a local reference center, it is called a point of interest, seven regions based on local reference points; (xj) and (yj) represents each point of interest in time and space [9].(3)n=argminai-xj2+bi-yj2xzi,yzi.


## 2.3. Feature Extraction of Foul Action

On the basis of the sequence potential function, the feature data of football players’ foul behavior are screened by enhancement algorithm, and the feature extraction of training and identifying football players’ foul behavior is realized. Suppose football player fouls, the action image training sample set is (x1, y1)…(xi, yi)…(xn, yn), where xi denotes foul action image sample data, yi denotes foul action image sample mark. The sample data of any foul action in the training sample N are used as known samples. The AdaBoost algorithm is a weak classifier, and the training sample is obtained. Suppose, based on φ(y, h, x), the sample error rate of the football player's foul action image is calculated, and formula (4) is obtained, where, εt represents the sample error rate [10].(4)εt=ϕY,h,X×htxi≠yix1,y1,⋯xi,yi,⋯xN,yN.According to the calculation results of formula (4), the features of the extracted foul action are marked as 1, and the rest are marked as −1. By the iterative method, the feature extraction model of football player's foul behavior is established. The representation of the model is as follows [11]:(5)C^i=argmindi−Cdj2∗εt.Thedi represents the i feature data in the foul action sequence, and the C (dj) represents the category of the foul action feature data in the training sample. According to formula (5), the feature extraction of the behavior of football players based on machine vision is realized.


## 2.4. Establishment of Membership Matrix

Clustering bottom foul action features usingK-mean, K, for cluster centers get K × 162-dimension foul action image, at the same time generate three levels and seven areas of foul action images, each area foul action T frame is defined as the space-time foul action module, represents the characteristics of a part of a football player’s foul behavior. When the foul action image unit of two overlapping T/2 frames are connected, all the action units in each foul action image are organically synthesized, a series of temporal and spatial foul action units with length, after k-mean clustering, the characteristics of the echo bow of the action-image measured by the foul action data set are obtained as formula (6), where fp represents the BOW features of foul action image p echo, Kn indicates the number of cluster centers in the foul range, and N represents the sequence length of the temporal and spatial units of the foul action image [12].(6)fp=Kn×NK∗K×162P.The fusion formula is BOW, whereFp represents the fusion characteristics of each region at the corresponding level.(7)FP=∑n∈1,7Kn×N.


## 3. Experiment

### 3.1. Experimental Data Sources

There are more than 200 papers on “athlete foul, soccer player foul”, and more than 20 studies on the foul characteristics of men’s, including 3 excellent master’s papers and 9 studies on the foul characteristics of female football players, including 1 excellent master’s thesis. However, there are few articles on the foul characteristics of male and female athletes. Therefore, this paper takes male and female athletes as the research object.


### 3.2. Experimental Design

This paper studies the foul characteristics of high-level athletes by means of literature, investigation, video observation, data statistics, and other scientific research methods through the technical statistical research on 57 fouls of women’s World Cup and 32 football teams in the European Cup.


## 3.1. Experimental Data Sources

There are more than 200 papers on “athlete foul, soccer player foul”, and more than 20 studies on the foul characteristics of men’s, including 3 excellent master’s papers and 9 studies on the foul characteristics of female football players, including 1 excellent master’s thesis. However, there are few articles on the foul characteristics of male and female athletes. Therefore, this paper takes male and female athletes as the research object.


## 3.2. Experimental Design

This paper studies the foul characteristics of high-level athletes by means of literature, investigation, video observation, data statistics, and other scientific research methods through the technical statistical research on 57 fouls of women’s World Cup and 32 football teams in the European Cup.


## 4. Result

### 4.1. Analysis of the Time Period When Male and Female Athletes Get Red and Yellow Cards

As can be seen from Figure1, the number of yellow cards obtained by male athletes in the fourth phase is up to 33, accounting for 28.45% of the total number of yellow cards. Continue to compete fiercely in the game, hoping to open the score as soon as possible to grasp the initiative. As shown in Figure 1, the peak of the red card appears in the last 15 min, indicating that the team athletes at all costs to achieve a victory or equal share, the psychology of winning and losing makes them use fierce, aggressive, and harmful defensive actions to prevent the other side from threatening attacks, even if the foul player is sent off by the referee to show the red card, it is difficult for the other side to make full use of the increasing advantages of the game.Figure 1
The proportion of red and yellow cards obtained by athletes in two cup tournaments in each time period.


### 4.2. Regional Analysis of Red and Yellow Cards for Athletes from Different Positions of Men and Women

Figure2 shows that the men’s forward and the guard gets 7 and 14 yellow cards in the midfield, 35% and 38.88%, respectively; the front and back of the avant-garde have 10 yellow cards and 17 yellow cards, accounting for 17.54% and 26.32% of the total number of yellow cards received by the avant-garde, which indicates that the defensive area of the men's forward and the guard is expanding, not just in your own area. The vanguard and the guard can go to the midfield to assist the vanguard to launch the fierce robbery together, the avant-garde team can cooperate with the forward team, so that the other side cannot pass easily, return to the backcourt area to help the guard set up the defense line, ensure that the target will not be lost.Figure 2
The number of yellow cards obtained by athletes in different positions of men and women.


### 4.3. Analysis of Foul Time Periods for Male and Female Athletes

We can see from Figure3 that the first, fourth, and sixth line breaking chart of the male foot athletes foul time all points are in the peak position, showing a gradual upward trend, these three points are the first 15 min of the upper and lower half, and the last 15 min which is close to the end of the game is the best time for players to foul.Figure 3
The proportion of fouls in each period of the two cup matches.The number of fouls in the first stage accounts for 17.43% of the total number of fouls. On the one hand, this shows that the top 15 athletes want to use their rich physical strength to exert their technical and tactical advantages, restrain their opponents’ skills and tactics, win active goals and win psychological advantages; on the other hand, as the law enforcers of the game, in order to better control and guide the game, the referee is more strict in the first 15 min, the start of the game, resulting in more first-half fouls in the first 15 min. The number of fouls in the fourth stage was 18.27, because the coach made tactical arrangements and adjustments in the second half, and the player recovered in the 15 min of midfield, so the players were more intense in the first 15 min of the second half and fought frequently, want to open up or win. The number of fouls in the sixth stage reached 19.01 because the last 15 min of the second half was the key stage of the game, the score could be rewritten in the last 15 min, the leading team wanted to keep the advantage and win the game, and when the last 15 min of the game fell, the defense slowed down.


### 4.4. Analysis of Foul Types of Male and Female Athletes

Table1 and Figure 4 show the proportion of foul types for women’s World Cup players in 2019. The way in which women footballers kick, push, grab, stumble, collide, and pull, account for 83.86% of the total number of fouls. One of the most serious fouls is playing or trying, there were 102 fouls, which is 18.72% of the total fouls, the second is to push and intercept each other which is 15.78%, trips account for 14.5%, collisions account for 10.83%, and pull each other accounts for 8.62%.Table 1
Statistics on the types of fouls committed by athletes in the final stage of the Women’s World Cup 2019.
Type of foulNumber of foulsPercentage (%)Deliberate handball203.67Beat or attempt to beat00Pull each other478.62Push an opponent8615.78Jump to each other (goalkeeper)458.25Obstruction (goalkeeper)112.02Grabbing each other8615.78Trip or attempt to stumble7914.5Kick or attempt to kick10218.72Spit on each other00Action is dangerous101.83Fake a fall00Hit the other side5910.83Amount to545100Figure 4
Percentage of indicators in the type of foul played by women football players.From the video game of the Women’s World Cup, it can be found that the speed and strength of players on the field compared with the last World Cup, especially in Germany, Brazil, and the United States, the physical fitness of athletes close to men’s football players, especially in close defense players, the ability to use body fighting and robbery increases the proportion of collisions and tripping fouls. From the analysis of defensive technology, the technique of snatching the ball is mainly done by the lower limbs, and the foul action in the process of snatching the ball should be mainly kicking, tripping, and snatching the other side. These fouls are offensive and can directly make the hitter fall and lose control and can play the purpose of preventing the other side from attacking. Due to the improvement of the statistical scale of the first two women’s football World Cup, the penalty for foul has increased, and the number of fouls with obvious effect has also shown a downward trend, which is why the proportion of pulling fouls is gradually decreasing.


### 4.5. Analysis of Experimental Environment and Parameters

In order to ensure the fairness of the test results, a foul action capture test room is set up, and three cameras are installed to obtain the action diagram. Parameter settings for machine vision system OV7670 model camera is shown in Table2.Table 2
OV7670 model camera parameters.
ParameterNumerical valueInput clock range/MHz6–48Color filter arrayRGB leaf patternResolution ratio10 million pixels3856H × 2764 V4 : 3: static mode3664H × 2748 V16 : 9: video mode3840H × 2160 VMaximum dataSerial/Gbps2.8Transmission speedParallel/mp/s80Data resolution/bit12Consumption/m W638SourceSLVSI/O/V0.4–0.8Analog voltage/V2.4–3.1Digital voltage/V1.7–1.9I/O digital voltage/V1.8 or 2.8


### 4.6. Accuracy of Feature Extraction of Fouls by Different Methods

The recognition parameters of foul behavior indirectly affect the accuracy of feature extraction of foul behavior. The larger the recognition parameters of foul action, the higher the accuracy of feature extraction. The identification parameters of foul behavior are obtained through experiments. The experimental results are shown in Table3.Table 3
Comparison of foul action recognition parameters.
Number of testsBased on HalconBased on FVRTSBased on machine visionFeature extractionFeatures of the platformFeature extractionMethodExtraction methodMethod of sense205.125.1210.23405.026.459.45605.006.009.84804.036.219.911004.106.3310.031204.206.4910.001404.446.459.021604.796.669.151803.986.949.452004.915.489.60The data in Table3 show that the range of identification parameters of foul behavior is 9.02–10.23, and that of traditional representative methods is 3.98–5.12 and 5.12–6.94, respectively. In contrast, the identification parameters of the proposed foul behavior are much higher than the traditional recognition method. This is because the method extracts the features of the foul image, establishes the potential function of the foul action sequence, and analyzes it in more detail. Thus, the accuracy of the method for the foul action recognition is improved.


## 4.1. Analysis of the Time Period When Male and Female Athletes Get Red and Yellow Cards

As can be seen from Figure1, the number of yellow cards obtained by male athletes in the fourth phase is up to 33, accounting for 28.45% of the total number of yellow cards. Continue to compete fiercely in the game, hoping to open the score as soon as possible to grasp the initiative. As shown in Figure 1, the peak of the red card appears in the last 15 min, indicating that the team athletes at all costs to achieve a victory or equal share, the psychology of winning and losing makes them use fierce, aggressive, and harmful defensive actions to prevent the other side from threatening attacks, even if the foul player is sent off by the referee to show the red card, it is difficult for the other side to make full use of the increasing advantages of the game.Figure 1
The proportion of red and yellow cards obtained by athletes in two cup tournaments in each time period.


## 4.2. Regional Analysis of Red and Yellow Cards for Athletes from Different Positions of Men and Women

Figure2 shows that the men’s forward and the guard gets 7 and 14 yellow cards in the midfield, 35% and 38.88%, respectively; the front and back of the avant-garde have 10 yellow cards and 17 yellow cards, accounting for 17.54% and 26.32% of the total number of yellow cards received by the avant-garde, which indicates that the defensive area of the men's forward and the guard is expanding, not just in your own area. The vanguard and the guard can go to the midfield to assist the vanguard to launch the fierce robbery together, the avant-garde team can cooperate with the forward team, so that the other side cannot pass easily, return to the backcourt area to help the guard set up the defense line, ensure that the target will not be lost.Figure 2
The number of yellow cards obtained by athletes in different positions of men and women.


## 4.3. Analysis of Foul Time Periods for Male and Female Athletes

We can see from Figure3 that the first, fourth, and sixth line breaking chart of the male foot athletes foul time all points are in the peak position, showing a gradual upward trend, these three points are the first 15 min of the upper and lower half, and the last 15 min which is close to the end of the game is the best time for players to foul.Figure 3
The proportion of fouls in each period of the two cup matches.The number of fouls in the first stage accounts for 17.43% of the total number of fouls. On the one hand, this shows that the top 15 athletes want to use their rich physical strength to exert their technical and tactical advantages, restrain their opponents’ skills and tactics, win active goals and win psychological advantages; on the other hand, as the law enforcers of the game, in order to better control and guide the game, the referee is more strict in the first 15 min, the start of the game, resulting in more first-half fouls in the first 15 min. The number of fouls in the fourth stage was 18.27, because the coach made tactical arrangements and adjustments in the second half, and the player recovered in the 15 min of midfield, so the players were more intense in the first 15 min of the second half and fought frequently, want to open up or win. The number of fouls in the sixth stage reached 19.01 because the last 15 min of the second half was the key stage of the game, the score could be rewritten in the last 15 min, the leading team wanted to keep the advantage and win the game, and when the last 15 min of the game fell, the defense slowed down.


## 4.4. Analysis of Foul Types of Male and Female Athletes

Table1 and Figure 4 show the proportion of foul types for women’s World Cup players in 2019. The way in which women footballers kick, push, grab, stumble, collide, and pull, account for 83.86% of the total number of fouls. One of the most serious fouls is playing or trying, there were 102 fouls, which is 18.72% of the total fouls, the second is to push and intercept each other which is 15.78%, trips account for 14.5%, collisions account for 10.83%, and pull each other accounts for 8.62%.Table 1
Statistics on the types of fouls committed by athletes in the final stage of the Women’s World Cup 2019.
Type of foulNumber of foulsPercentage (%)Deliberate handball203.67Beat or attempt to beat00Pull each other478.62Push an opponent8615.78Jump to each other (goalkeeper)458.25Obstruction (goalkeeper)112.02Grabbing each other8615.78Trip or attempt to stumble7914.5Kick or attempt to kick10218.72Spit on each other00Action is dangerous101.83Fake a fall00Hit the other side5910.83Amount to545100Figure 4
Percentage of indicators in the type of foul played by women football players.From the video game of the Women’s World Cup, it can be found that the speed and strength of players on the field compared with the last World Cup, especially in Germany, Brazil, and the United States, the physical fitness of athletes close to men’s football players, especially in close defense players, the ability to use body fighting and robbery increases the proportion of collisions and tripping fouls. From the analysis of defensive technology, the technique of snatching the ball is mainly done by the lower limbs, and the foul action in the process of snatching the ball should be mainly kicking, tripping, and snatching the other side. These fouls are offensive and can directly make the hitter fall and lose control and can play the purpose of preventing the other side from attacking. Due to the improvement of the statistical scale of the first two women’s football World Cup, the penalty for foul has increased, and the number of fouls with obvious effect has also shown a downward trend, which is why the proportion of pulling fouls is gradually decreasing.


## 4.5. Analysis of Experimental Environment and Parameters

In order to ensure the fairness of the test results, a foul action capture test room is set up, and three cameras are installed to obtain the action diagram. Parameter settings for machine vision system OV7670 model camera is shown in Table2.Table 2
OV7670 model camera parameters.
ParameterNumerical valueInput clock range/MHz6–48Color filter arrayRGB leaf patternResolution ratio10 million pixels3856H × 2764 V4 : 3: static mode3664H × 2748 V16 : 9: video mode3840H × 2160 VMaximum dataSerial/Gbps2.8Transmission speedParallel/mp/s80Data resolution/bit12Consumption/m W638SourceSLVSI/O/V0.4–0.8Analog voltage/V2.4–3.1Digital voltage/V1.7–1.9I/O digital voltage/V1.8 or 2.8


## 4.6. Accuracy of Feature Extraction of Fouls by Different Methods

The recognition parameters of foul behavior indirectly affect the accuracy of feature extraction of foul behavior. The larger the recognition parameters of foul action, the higher the accuracy of feature extraction. The identification parameters of foul behavior are obtained through experiments. The experimental results are shown in Table3.Table 3
Comparison of foul action recognition parameters.
Number of testsBased on HalconBased on FVRTSBased on machine visionFeature extractionFeatures of the platformFeature extractionMethodExtraction methodMethod of sense205.125.1210.23405.026.459.45605.006.009.84804.036.219.911004.106.3310.031204.206.4910.001404.446.459.021604.796.669.151803.986.949.452004.915.489.60The data in Table3 show that the range of identification parameters of foul behavior is 9.02–10.23, and that of traditional representative methods is 3.98–5.12 and 5.12–6.94, respectively. In contrast, the identification parameters of the proposed foul behavior are much higher than the traditional recognition method. This is because the method extracts the features of the foul image, establishes the potential function of the foul action sequence, and analyzes it in more detail. Thus, the accuracy of the method for the foul action recognition is improved.


## 5. Conclusion

In this paper, a method based on machine vision to extract the foul behavior characteristics of football players is proposed. By obtaining images and analyzing image features, the characteristics of foul behavior of football players are extracted. The simulation results show that the method can effectively identify the foul behavior of football players. The traditional football player’s foul action feature extraction method is to describe the foul behavior as a series of symbols, decompose each symbol, and identify the foul action feature. However, the method has the problem of low feature extraction accuracy in high-level football games. In order to solve the above problems, a method based on machine vision is proposed to extract the foul action characteristics of football players. Machine vision is a branch of the development of artificial intelligence. The target is converted into an image signal mainly through machine vision products and transmitted to the special image processing system.



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