# Visual Management of Sports Based on Intelligent Analysis of Big Data

Authors: Danya Yang; Jianfang Wang; Longlong Liu
Journal: Mobile Information Systems
 (2022)

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## Abstract

After years of development, big data technology has become more mature and reliable in its application in various fields. With the explosive growth of data and the rise of data complexity, the processing and application of data have actually changed our daily life. The development of data in sports is very active in recent years, and the natural dependence of sports on data determines that the visualization of sports has a great creative space in data sports. Therefore, it is necessary to deeply study the visual management of sports under the background of big data technology. As we all know, in sports, competitive games occupy the vast majority, and the data generated by this is not limited to the score of the game. People are paying more and more attention to information related to sports, but these information are generally very complicated and huge. This is also an area with the widest audience, and the core of competitive sports is data. The final results and progress of most sports competitions are reflected through changes and comparisons of data. With the continuous development of big data information collection technology, the available sports data are more detailed, which also makes sports develop toward visualization. Therefore, with the help of the excellent ability of big data in screening data, this article proposes algorithms such as DTW and recurrent neural networks to reasonably and reliably analyze and process a large number of data generated in the process of sports and embeds an error analysis module in the designed model to ensure the accuracy requirements in data processing to a greater extent. The model designed in this article has been able to improve the accuracy by more than 86%. This will greatly facilitate our management and have a far-reaching impact on sports visualization.


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## Body

## 1. Introduction

Big data technology has matured and become more reliable in various fields after years of development [1]. The processing and application of data have changed our daily lives due to the explosive growth and the increasing complexity of data [2]. The impact of the big data era has permeated our lives. Today’s society has entered the era of large-scale data production, application, and dissemination, thanks to the continued development and maturity of data acquisition and visualization technology [3]. Data visualization has become a problem that people must consider. As a result, in the age of big data, data visualization has emerged as an important research field in modern information science.In the end, the essence of today’s global competition is a contest between talents and science and technology, the quantity and quality of talents, and the strength of science and technology, all of which are directly related to whether a country or region can maintain a dominant position and take the initiative in competition [4]. Sports can significantly improve people’s physical fitness. It is not only a basic livelihood project that benefits the people’s livelihood, but it is also a critical component of the country’s strategy of strengthening the country through science and technology, talent development, and sports modernization. The party and the state place a high value on sports development [5]. Motion visualization is a technique that converts motion data into digital images and displays them on smart screens using advanced computer network engineering and image processing technology. Motion no longer appears in front of the public in the form of words, but rather in the form of visualization, which conveys the motion with visual impact to the public [6]. With the advancement of technology, the definition of motion visualization is constantly being refined.With the rapid advancement of data collection technology in the era of big data, the sports data we can obtain are becoming more accurate, and the types of data are becoming more detailed, allowing us to make more attempts with data and bringing more possibilities [7]. Under such circumstances, sports visualization research was born. Each competitor has their own competitive strength for each sports competition, excluding psychological and accidental factors, and the data can better grasp the process and development of the competition, attract the audience’s attention, and let the audience know the more real competition situation [8]. The ability and strength of athletes or sports teams can be better assessed using data [9]. Data visualization is the most effective presentation method for data movement, as it allows the audience to see both boring and complicated data. Through data movement visualization, the audience can not only learn about the status of sports events but also learn more clearly and logically about the specific event analysis of the competition, to meet the audience’s demand for sports.Data analysis against the backdrop of big data is more reliable and efficient than traditional sports data analysis. In this context, sports data research is gradually increasing, with data visualization being an important component of sports data, and there is no shortage of related research in academia and industry [10]. These studies primarily concentrate on two aspects: on the one hand, analysis and research are conducted from the perspective of data processing and visualization technology, and on the other hand, analysis is conducted from the change of movement data to data production ideas and processes, beginning with specific cases of movement data visualization [11]. Some of these studies, on the one hand, have a macro understanding of data visualization technology, while others focus on individual movements. They both are not sufficiently integrated. As a result of the foregoing, this article proposes the following innovations:(1)
Based on the data analysis ability in the context of big data, this article conducts visual research and management of sports in different spatial dimensions, quantitative analysis, and trajectory modeling of sports, and uses dynamic motion functions and spatial dimension equations to mine and process data.(2)
In addition, based on the above data processing methods, further filtering, simplifying, and aggregating of the visualization data under the data processing methods proposed in this article, it will make the visualization of sports more reasonable and rigorous, and play a good role in management.


## 2. Related Work

Data visualization has been well applied in the field of sports under the background of increasingly mature big data technology. Liu et al. mentioned in the article “Opportunities, Challenges and Changes in the Development of Journalism in the Era of ‘Big Data,’” that the information and technological progress in the era of big data has brought massive amounts of data to the news media industry. Faced with such a huge amount of data, the media industry has transformed the way of news analysis and mining, and transformed the traditional way of news dissemination into a way of data news dissemination [12]. Golelli and Rizzis proposed that through certain information mining technology, we can find the subtle relationships and valuable contents behind these seemingly meaningless things [13]. The research by Zhu and Liu shows that the collection and mining of big data have opened up a new field for sports visualization. In terms of the content of sports, traditional sports require relevant workers to conduct on-the-spot interviews, form text drafts or video films, and then come out after post-editing processing [14]. Cao and Ren proposed that from the perspective of sports performance, big data sports are presented through visualization. These visualization methods make the previously monotonous and abstract data more acceptable to the audience [15]. The research by Pang et al. shows that human beings have entered the information age, and people tend to collectively refer to all the information stored in the computer as data because human beings can analyze and utilize it, discover new content, and create new value, This is called big data [16]. Chen et al. put forward that the application of data has become the greatest advantage of online sports. Applying statistical analysis of data to sports data and changing the original structure of sports is one of the first successful changes made by online sports [17]. Wee pointed out that in the era of big data, we should deal it with big data thinking, which is mainly reflected in three aspects: in the era of big data, we can analyze more data instead of relying on follow up sampling; information analysis is no longer keen on the pursuit of accuracy, but a hybrid; and no longer keen on looking for causality, but looking for the correlation between things [18]. Floriani and Leila believe that data journalism is not a single concept, but a general term in the Data Journalism Handbook, which covers a series of novel analytical tools and technical methods. Mueller proposed a spatiotemporal analysis framework to explore crowd flow patterns and urban internal dynamics. Based on the detailed records of large-scale mobile phones in the city, combined with spatiotemporal density estimation, spatiotemporal autocorrelation analysis, and spatiotemporal visualization, the spatiotemporal data are intuitively expressed, and quantitative analysis is also provided to determine the spatiotemporal pattern of crowd flow. However, the above methods, usually, only focus on specific attributes and do not provide sufficient visual support to study the spatial, temporal, or other attribute components of tracks in 3D geospatial space [19]. Hong Big Data Analytics: making the Smart Grider thinks that the remarkable reality of big data requires people to look at it and deal with it with brand-new information thinking. When the stall and reproduction of information approach the limit that people can bear, information may become a burden [20]. Xu et al. believe that data sports enable anyone to penetrate the data source, and people should publish structured, machine-readable data, putting aside the traditional “lots of text,” data-centric sports form [21]. Zhu et al. developed the geozuid4d system, which uses color, texture, and font to display multiple attributes of the 3D trajectory of underwater moving objects, but does not use some form of aggregation to simplify the data, so there are still problems such as excessive drawing and view occlusion [22].Based on the research of related work mentioned above, this article determines the positive role of big data analysis technology in the field of visual management of sports, constructs a visual model of sports data combined with various technologies, makes deep analysis and research on the acquired and collected data by using big data technology, makes more effective use of the data, and excavates valuable information hidden behind the data, to make the management simple and efficient.


## 3. Methodology

### 3.1. Overview of Big Data Technology

Big data is a collection of data with a large capacity, a diverse set of types, quick access, and high application value. It is a new generation of information technology and service formats for storing and analyzing associations to discover new knowledge, create new value, and improve capabilities [23]. To conduct distributed data mining for massive data, big data requires a distributed architecture, which requires cloud computing, distributed database, cloud storage, and virtualization technology [24]. The information data that people want to get from sports data processing is obtained through the analysis and processing of the correlation between a variety of complex data on the competition field, such as scoring data, foul data, and athlete’s body information data [25]. The visual processing and analysis process in sports is depicted in Figure 1.Figure 1
The abstract staggered analysis process of visual analysis.Trajectory data visualization is to model with the help of computer graphics, computer vision, user interface, and other technologies, expresses the trajectory data in visual form, and provides an effective interactive way to support the user’s data exploration behavior. Trajectory visualization plays an important role in the analysis and cognition of large-scale complex multidimensional trajectory data, which has the characteristics of intuition, integration, dynamics, and interactivity [26]. Therefore, this article divides the visualization analysis into three types: intuitive visualization, aggregation visualization, and feature visualization. For these three different visualization methods, visual visualization has the advantages of data and interactive extraction. Aggregation visualization plays a good role in data aggregation and summary. Feature visualization generally involves data mining technology. For data features, it needs a series of processing flows such as analysis, classification, filtering, and aggregation, which has a good effect in certain cases.


### 3.2. Methods of Analyzing Sports Data

#### 3.2.1. Quantitative Analysis Method of Motion Data

Quantitative analysis is a method of conducting systematic research and analysis of observable phenomena that primarily employs statistics, mathematics, probability theory, and computer technology, with the goal of developing and applying mathematical models and theories related to phenomena. By examining the quantitative characteristics, quantitative relationships, and quantitative changes of motion phenomena, quantitative analysis reveals and describes the interaction and development trend of phenomena. This method can also be used to quantitatively analyze and compare some of the properties, characteristics, associations, or causal relationships between several objects, with the final research results being expressed in numerical form. We often need to study the moving object itself when studying sports goals, so we need to describe the quantitative indicators of moving objects and create a dimension based on sports characteristics measurement. As shown in Figure2, different kinds of trajectory movements are described.Figure 2
Different types of trajectory motion.The general motion description symbols are calculated on the data set of motion trajectory. In this article, motion description is divided into two categories: original parameters and derived parameters. Because the above two parameters can be organized in both the time dimension and the spatial dimension, it is feasible to express the correlation coefficient of the spatial dimension and the time dimension by speed and rate, and it can be directly calculated by using the spatial position and time parameters.


#### 3.2.2. Qualitative Visual Analysis Method of Motion Data

In terms of qualitative analysis, it primarily uses volume visualization technology to create three-dimensional visualizations of stacked space-time density volume and space density volume. The trajectory’s spatial density creates a volume made up of multiple voxels, each with its own scalar field value. The abbreviation voxel stands for volume element, which represents a single sample or data point on a three-dimensional grid with regular spacing. A voxel represents a single point on that grid, not a volume, and this data point can consist of a single data point. In the two dimensions of a computer screen, density volume data is difficult to visualize. Visual relationships such as inclusion, occlusion, and superposition will emerge from the simultaneous display of many density volumes. Volume visualization is an important branch of data visualization research.


### 3.3. Sports Visualization Model Based on Big Data Analysis Technology

#### 3.3.1. Model Building

Based on the powerful ability and reliability of big data analysis, this article proposes a sports visualization model to assist management. The system architecture is divided into five layers: application, data processing, communication, data acquisition, and hardware. The hardware layer includes the physical power supply, sensor, and depth camera for collecting 3D data. The data collection layer is responsible for categorizing, integrating, and analyzing the data collected by the physical layer. The data upload at the bottom is implemented using the protocol by the communication layer. The data processing layer is in charge of ensuring data sequence and preservation, as well as some preprocessing to filter out useless data; the application layer is in charge of calculating and processing data, as well as assisting in action scoring. Figure3 shows the model’s fundamental architecture.Figure 3
Basic structure of the model.


#### 3.3.2. Feature Extraction Based on DTW

DTW is a common distance measurement method for time series similarity estimation. Two symbol sequencesrtx′rty were set and their lower limit distance is:(1)Elowerrtx,rty=na+nb2×j∑j=1jϕaj,bj2.In the formula, the length of the sports dataa,b is na′nb in sequence, the dimension of the athlete’s action data is j, and ϕaj,bj is the Euclidean distance of the j-dimension sports data aj,bj.(2)ϕaj,bj=Cmaxaj,bj−Cminaj,bj.In the above formula,Cmaxaj,bjandCminaj,bj represents the maximum and minimum breakpoint of j-dimensional motion data aj,bj, respectively.(3)Ei,j=∑j=1jaj−bj2.Action data points belong to three-dimensional data at all times. You can select one-time matching or matching mode in each dimension according to the relationship between difference dimensions. Two matching patterns can obtain different action features,Ga,Gb, respectively. Ga’s perception of action is more significant.


#### 3.3.3. Action Recognition Analysis

Recurrent neural networks are very important in the field of motion recognition. On the one hand, the cyclic neural network uses the RGB picture of a single video frame as an input to extract the video’s spatial surface information; on the other hand, it uses the multiframe stacked optical flow graph as an input to extract the video’s motion control information. When people watch and understand the video, this complementary mode of the two is also very consistent.Bivariate probabilistic models in which motion locations represent point samples from a two-dimensional probability density function are used to derive density-based methods in spatial point pattern analysis. This article proposes to redefine KDE as a data mining technology that aims to group data into unrepeatable pattern groups based on event or phenomenon similarity. KDE calculates the event density and spatial distribution at each point by generating a smooth and continuous surface from the point pattern. The spatial intensity of each point in the study area is calculated with the kernel function. According to the distance between each point and the event, the function calculates each point’s contribution to the event. We get(4)f^X=1nh2∑i=1nKX−Xih,where n is the number of sample points, K is the kernel function defined in the two-dimensional space, X is the point x,y where the density is to be estimated, h is the smoothing parameter, and Xi0≤i≤n is the sample point within the bandwidth h. K is any continuous, non-negative, and radially symmetric kernel function integrating to 1 as follows:(5)f^Linearx=3πh2h−xh,ifx≤h,0,ifx≤h,f^Bisquarex=3πh2x2−h22h4,ifx≤h,0,ifx>h,f^Gaussianx=12πσ2⋅e−x2/2σ2,=22πh2⋅e−2x2/h2.σ2=h/22 set in the formula can be designed according to specific conditions. The density surface produced is heavily influenced by the kernel bandwidth chosen. If the bandwidth is too wide, the estimated density will be consistent throughout the study area and close to the average density. The density distribution will gather at each point if the bandwidth is too small. To calculate the optimal bandwidth settings for a satisfactory density surface, experimentation is required.In terms of neural networks, the circular neural network proposed in this article can evolve into both a long-term and short-term memory networks, so it is made up of several identical units, which are also essential for dealing with long-term data. The forgetting gate is first introduced. In both long-term and short-term memory networks, the forgetting gate is the most important unit. It is essential for preventing gradient disappearance and explosion. The sigmoid function is primarily responsible for this. It investigates the relationshipe between the previous unit’s output information and the input information for this unit, and achieves information filtering by using numbers between 0 and 1. When the number is zero, the information is completely discarded, while when the number is one, the information is completely retained. When the number is between 0 and 1, it means that some information is discarded and some is retained. The implementation formula isσ(6)ft=σWfht−1,xt+bf,where σ represents sigmoid function operation.Then use the input gate to determine the input information that this unit needs to store, but not all input information will be stored, only the information that passes the input gate will be stored and participate in subsequent calculations, and the information that does not pass will be filtered. The input information is determined by the function, then the important information of the previous unit is filtered by the forgetting gate, the input information is filtered by the input gate, and then the processed unit information is obtained, which represents the information state in the cell, as shown in the following formula:(7)it=σWiht−1,xt+bi,C˜t=tanhWcht−1,xt+bC,Ct=fi∗Ct−1+it∗C˜t.Finally, it is calculated through the input information:(8)ot=σWoht−1,xt+ba,ht=ot∗tanhCt.Long- and short-term memory networks have played a great advantage in long-term modeling due to their transferable, modifiable, and forgettable properties, and are also widely used in speech recognition, text translation, and video understanding. The key to the action recognition task is to extract and utilize all kinds of information in the video as much as possible, to improve the utilization rate of video information, and finally to make action recognition more accurate and robust. Through the above-proposed model algorithm, this article can provide great help in the accuracy and reliability of exit analysis and get rid of the imprecision of visual analysis of sports in the original system to a certain extent.


## 3.1. Overview of Big Data Technology

Big data is a collection of data with a large capacity, a diverse set of types, quick access, and high application value. It is a new generation of information technology and service formats for storing and analyzing associations to discover new knowledge, create new value, and improve capabilities [23]. To conduct distributed data mining for massive data, big data requires a distributed architecture, which requires cloud computing, distributed database, cloud storage, and virtualization technology [24]. The information data that people want to get from sports data processing is obtained through the analysis and processing of the correlation between a variety of complex data on the competition field, such as scoring data, foul data, and athlete’s body information data [25]. The visual processing and analysis process in sports is depicted in Figure 1.Figure 1
The abstract staggered analysis process of visual analysis.Trajectory data visualization is to model with the help of computer graphics, computer vision, user interface, and other technologies, expresses the trajectory data in visual form, and provides an effective interactive way to support the user’s data exploration behavior. Trajectory visualization plays an important role in the analysis and cognition of large-scale complex multidimensional trajectory data, which has the characteristics of intuition, integration, dynamics, and interactivity [26]. Therefore, this article divides the visualization analysis into three types: intuitive visualization, aggregation visualization, and feature visualization. For these three different visualization methods, visual visualization has the advantages of data and interactive extraction. Aggregation visualization plays a good role in data aggregation and summary. Feature visualization generally involves data mining technology. For data features, it needs a series of processing flows such as analysis, classification, filtering, and aggregation, which has a good effect in certain cases.


## 3.2. Methods of Analyzing Sports Data

### 3.2.1. Quantitative Analysis Method of Motion Data

Quantitative analysis is a method of conducting systematic research and analysis of observable phenomena that primarily employs statistics, mathematics, probability theory, and computer technology, with the goal of developing and applying mathematical models and theories related to phenomena. By examining the quantitative characteristics, quantitative relationships, and quantitative changes of motion phenomena, quantitative analysis reveals and describes the interaction and development trend of phenomena. This method can also be used to quantitatively analyze and compare some of the properties, characteristics, associations, or causal relationships between several objects, with the final research results being expressed in numerical form. We often need to study the moving object itself when studying sports goals, so we need to describe the quantitative indicators of moving objects and create a dimension based on sports characteristics measurement. As shown in Figure2, different kinds of trajectory movements are described.Figure 2
Different types of trajectory motion.The general motion description symbols are calculated on the data set of motion trajectory. In this article, motion description is divided into two categories: original parameters and derived parameters. Because the above two parameters can be organized in both the time dimension and the spatial dimension, it is feasible to express the correlation coefficient of the spatial dimension and the time dimension by speed and rate, and it can be directly calculated by using the spatial position and time parameters.


### 3.2.2. Qualitative Visual Analysis Method of Motion Data

In terms of qualitative analysis, it primarily uses volume visualization technology to create three-dimensional visualizations of stacked space-time density volume and space density volume. The trajectory’s spatial density creates a volume made up of multiple voxels, each with its own scalar field value. The abbreviation voxel stands for volume element, which represents a single sample or data point on a three-dimensional grid with regular spacing. A voxel represents a single point on that grid, not a volume, and this data point can consist of a single data point. In the two dimensions of a computer screen, density volume data is difficult to visualize. Visual relationships such as inclusion, occlusion, and superposition will emerge from the simultaneous display of many density volumes. Volume visualization is an important branch of data visualization research.


## 3.2.1. Quantitative Analysis Method of Motion Data

Quantitative analysis is a method of conducting systematic research and analysis of observable phenomena that primarily employs statistics, mathematics, probability theory, and computer technology, with the goal of developing and applying mathematical models and theories related to phenomena. By examining the quantitative characteristics, quantitative relationships, and quantitative changes of motion phenomena, quantitative analysis reveals and describes the interaction and development trend of phenomena. This method can also be used to quantitatively analyze and compare some of the properties, characteristics, associations, or causal relationships between several objects, with the final research results being expressed in numerical form. We often need to study the moving object itself when studying sports goals, so we need to describe the quantitative indicators of moving objects and create a dimension based on sports characteristics measurement. As shown in Figure2, different kinds of trajectory movements are described.Figure 2
Different types of trajectory motion.The general motion description symbols are calculated on the data set of motion trajectory. In this article, motion description is divided into two categories: original parameters and derived parameters. Because the above two parameters can be organized in both the time dimension and the spatial dimension, it is feasible to express the correlation coefficient of the spatial dimension and the time dimension by speed and rate, and it can be directly calculated by using the spatial position and time parameters.


## 3.2.2. Qualitative Visual Analysis Method of Motion Data

In terms of qualitative analysis, it primarily uses volume visualization technology to create three-dimensional visualizations of stacked space-time density volume and space density volume. The trajectory’s spatial density creates a volume made up of multiple voxels, each with its own scalar field value. The abbreviation voxel stands for volume element, which represents a single sample or data point on a three-dimensional grid with regular spacing. A voxel represents a single point on that grid, not a volume, and this data point can consist of a single data point. In the two dimensions of a computer screen, density volume data is difficult to visualize. Visual relationships such as inclusion, occlusion, and superposition will emerge from the simultaneous display of many density volumes. Volume visualization is an important branch of data visualization research.


## 3.3. Sports Visualization Model Based on Big Data Analysis Technology

### 3.3.1. Model Building

Based on the powerful ability and reliability of big data analysis, this article proposes a sports visualization model to assist management. The system architecture is divided into five layers: application, data processing, communication, data acquisition, and hardware. The hardware layer includes the physical power supply, sensor, and depth camera for collecting 3D data. The data collection layer is responsible for categorizing, integrating, and analyzing the data collected by the physical layer. The data upload at the bottom is implemented using the protocol by the communication layer. The data processing layer is in charge of ensuring data sequence and preservation, as well as some preprocessing to filter out useless data; the application layer is in charge of calculating and processing data, as well as assisting in action scoring. Figure3 shows the model’s fundamental architecture.Figure 3
Basic structure of the model.


### 3.3.2. Feature Extraction Based on DTW

DTW is a common distance measurement method for time series similarity estimation. Two symbol sequencesrtx′rty were set and their lower limit distance is:(1)Elowerrtx,rty=na+nb2×j∑j=1jϕaj,bj2.In the formula, the length of the sports dataa,b is na′nb in sequence, the dimension of the athlete’s action data is j, and ϕaj,bj is the Euclidean distance of the j-dimension sports data aj,bj.(2)ϕaj,bj=Cmaxaj,bj−Cminaj,bj.In the above formula,Cmaxaj,bjandCminaj,bj represents the maximum and minimum breakpoint of j-dimensional motion data aj,bj, respectively.(3)Ei,j=∑j=1jaj−bj2.Action data points belong to three-dimensional data at all times. You can select one-time matching or matching mode in each dimension according to the relationship between difference dimensions. Two matching patterns can obtain different action features,Ga,Gb, respectively. Ga’s perception of action is more significant.


### 3.3.3. Action Recognition Analysis

Recurrent neural networks are very important in the field of motion recognition. On the one hand, the cyclic neural network uses the RGB picture of a single video frame as an input to extract the video’s spatial surface information; on the other hand, it uses the multiframe stacked optical flow graph as an input to extract the video’s motion control information. When people watch and understand the video, this complementary mode of the two is also very consistent.Bivariate probabilistic models in which motion locations represent point samples from a two-dimensional probability density function are used to derive density-based methods in spatial point pattern analysis. This article proposes to redefine KDE as a data mining technology that aims to group data into unrepeatable pattern groups based on event or phenomenon similarity. KDE calculates the event density and spatial distribution at each point by generating a smooth and continuous surface from the point pattern. The spatial intensity of each point in the study area is calculated with the kernel function. According to the distance between each point and the event, the function calculates each point’s contribution to the event. We get(4)f^X=1nh2∑i=1nKX−Xih,where n is the number of sample points, K is the kernel function defined in the two-dimensional space, X is the point x,y where the density is to be estimated, h is the smoothing parameter, and Xi0≤i≤n is the sample point within the bandwidth h. K is any continuous, non-negative, and radially symmetric kernel function integrating to 1 as follows:(5)f^Linearx=3πh2h−xh,ifx≤h,0,ifx≤h,f^Bisquarex=3πh2x2−h22h4,ifx≤h,0,ifx>h,f^Gaussianx=12πσ2⋅e−x2/2σ2,=22πh2⋅e−2x2/h2.σ2=h/22 set in the formula can be designed according to specific conditions. The density surface produced is heavily influenced by the kernel bandwidth chosen. If the bandwidth is too wide, the estimated density will be consistent throughout the study area and close to the average density. The density distribution will gather at each point if the bandwidth is too small. To calculate the optimal bandwidth settings for a satisfactory density surface, experimentation is required.In terms of neural networks, the circular neural network proposed in this article can evolve into both a long-term and short-term memory networks, so it is made up of several identical units, which are also essential for dealing with long-term data. The forgetting gate is first introduced. In both long-term and short-term memory networks, the forgetting gate is the most important unit. It is essential for preventing gradient disappearance and explosion. The sigmoid function is primarily responsible for this. It investigates the relationshipe between the previous unit’s output information and the input information for this unit, and achieves information filtering by using numbers between 0 and 1. When the number is zero, the information is completely discarded, while when the number is one, the information is completely retained. When the number is between 0 and 1, it means that some information is discarded and some is retained. The implementation formula isσ(6)ft=σWfht−1,xt+bf,where σ represents sigmoid function operation.Then use the input gate to determine the input information that this unit needs to store, but not all input information will be stored, only the information that passes the input gate will be stored and participate in subsequent calculations, and the information that does not pass will be filtered. The input information is determined by the function, then the important information of the previous unit is filtered by the forgetting gate, the input information is filtered by the input gate, and then the processed unit information is obtained, which represents the information state in the cell, as shown in the following formula:(7)it=σWiht−1,xt+bi,C˜t=tanhWcht−1,xt+bC,Ct=fi∗Ct−1+it∗C˜t.Finally, it is calculated through the input information:(8)ot=σWoht−1,xt+ba,ht=ot∗tanhCt.Long- and short-term memory networks have played a great advantage in long-term modeling due to their transferable, modifiable, and forgettable properties, and are also widely used in speech recognition, text translation, and video understanding. The key to the action recognition task is to extract and utilize all kinds of information in the video as much as possible, to improve the utilization rate of video information, and finally to make action recognition more accurate and robust. Through the above-proposed model algorithm, this article can provide great help in the accuracy and reliability of exit analysis and get rid of the imprecision of visual analysis of sports in the original system to a certain extent.


## 3.3.1. Model Building

Based on the powerful ability and reliability of big data analysis, this article proposes a sports visualization model to assist management. The system architecture is divided into five layers: application, data processing, communication, data acquisition, and hardware. The hardware layer includes the physical power supply, sensor, and depth camera for collecting 3D data. The data collection layer is responsible for categorizing, integrating, and analyzing the data collected by the physical layer. The data upload at the bottom is implemented using the protocol by the communication layer. The data processing layer is in charge of ensuring data sequence and preservation, as well as some preprocessing to filter out useless data; the application layer is in charge of calculating and processing data, as well as assisting in action scoring. Figure3 shows the model’s fundamental architecture.Figure 3
Basic structure of the model.


## 3.3.2. Feature Extraction Based on DTW

DTW is a common distance measurement method for time series similarity estimation. Two symbol sequencesrtx′rty were set and their lower limit distance is:(1)Elowerrtx,rty=na+nb2×j∑j=1jϕaj,bj2.In the formula, the length of the sports dataa,b is na′nb in sequence, the dimension of the athlete’s action data is j, and ϕaj,bj is the Euclidean distance of the j-dimension sports data aj,bj.(2)ϕaj,bj=Cmaxaj,bj−Cminaj,bj.In the above formula,Cmaxaj,bjandCminaj,bj represents the maximum and minimum breakpoint of j-dimensional motion data aj,bj, respectively.(3)Ei,j=∑j=1jaj−bj2.Action data points belong to three-dimensional data at all times. You can select one-time matching or matching mode in each dimension according to the relationship between difference dimensions. Two matching patterns can obtain different action features,Ga,Gb, respectively. Ga’s perception of action is more significant.


## 3.3.3. Action Recognition Analysis

Recurrent neural networks are very important in the field of motion recognition. On the one hand, the cyclic neural network uses the RGB picture of a single video frame as an input to extract the video’s spatial surface information; on the other hand, it uses the multiframe stacked optical flow graph as an input to extract the video’s motion control information. When people watch and understand the video, this complementary mode of the two is also very consistent.Bivariate probabilistic models in which motion locations represent point samples from a two-dimensional probability density function are used to derive density-based methods in spatial point pattern analysis. This article proposes to redefine KDE as a data mining technology that aims to group data into unrepeatable pattern groups based on event or phenomenon similarity. KDE calculates the event density and spatial distribution at each point by generating a smooth and continuous surface from the point pattern. The spatial intensity of each point in the study area is calculated with the kernel function. According to the distance between each point and the event, the function calculates each point’s contribution to the event. We get(4)f^X=1nh2∑i=1nKX−Xih,where n is the number of sample points, K is the kernel function defined in the two-dimensional space, X is the point x,y where the density is to be estimated, h is the smoothing parameter, and Xi0≤i≤n is the sample point within the bandwidth h. K is any continuous, non-negative, and radially symmetric kernel function integrating to 1 as follows:(5)f^Linearx=3πh2h−xh,ifx≤h,0,ifx≤h,f^Bisquarex=3πh2x2−h22h4,ifx≤h,0,ifx>h,f^Gaussianx=12πσ2⋅e−x2/2σ2,=22πh2⋅e−2x2/h2.σ2=h/22 set in the formula can be designed according to specific conditions. The density surface produced is heavily influenced by the kernel bandwidth chosen. If the bandwidth is too wide, the estimated density will be consistent throughout the study area and close to the average density. The density distribution will gather at each point if the bandwidth is too small. To calculate the optimal bandwidth settings for a satisfactory density surface, experimentation is required.In terms of neural networks, the circular neural network proposed in this article can evolve into both a long-term and short-term memory networks, so it is made up of several identical units, which are also essential for dealing with long-term data. The forgetting gate is first introduced. In both long-term and short-term memory networks, the forgetting gate is the most important unit. It is essential for preventing gradient disappearance and explosion. The sigmoid function is primarily responsible for this. It investigates the relationshipe between the previous unit’s output information and the input information for this unit, and achieves information filtering by using numbers between 0 and 1. When the number is zero, the information is completely discarded, while when the number is one, the information is completely retained. When the number is between 0 and 1, it means that some information is discarded and some is retained. The implementation formula isσ(6)ft=σWfht−1,xt+bf,where σ represents sigmoid function operation.Then use the input gate to determine the input information that this unit needs to store, but not all input information will be stored, only the information that passes the input gate will be stored and participate in subsequent calculations, and the information that does not pass will be filtered. The input information is determined by the function, then the important information of the previous unit is filtered by the forgetting gate, the input information is filtered by the input gate, and then the processed unit information is obtained, which represents the information state in the cell, as shown in the following formula:(7)it=σWiht−1,xt+bi,C˜t=tanhWcht−1,xt+bC,Ct=fi∗Ct−1+it∗C˜t.Finally, it is calculated through the input information:(8)ot=σWoht−1,xt+ba,ht=ot∗tanhCt.Long- and short-term memory networks have played a great advantage in long-term modeling due to their transferable, modifiable, and forgettable properties, and are also widely used in speech recognition, text translation, and video understanding. The key to the action recognition task is to extract and utilize all kinds of information in the video as much as possible, to improve the utilization rate of video information, and finally to make action recognition more accurate and robust. Through the above-proposed model algorithm, this article can provide great help in the accuracy and reliability of exit analysis and get rid of the imprecision of visual analysis of sports in the original system to a certain extent.


## 4. Result Analysis and Discussion

Based on the above model, this article conducts experiments on the model and tests the rationality and reliability of the model and algorithm through data analysis, as well as the operability of sports visual management. The average recall rate is compared and analyzed, as shown in Figure4.Figure 4
The relationship between the average recall rate and the number of proposals.Among them, the solid line represents the average value of the average recall rate corresponding to ten values selected when the intersection and merger ratio is between 0.4 and 0.9, while the virtual line shows the average recall rate under the fixed intersection and merger ratio, including 0.4, 0.5, 0.6, 0.8, and 1.0, and calculates the area under the curve of the average recall rate under these five cases. It can be seen that the smaller the intersection ratio, the larger the corresponding area under the average recall curve. In addition, this article analyzes the speed and heat consumption of a target moving in the actual situation, and the experimental results are shown in Figure5.Figure 5
Analysis of the movement speed.Through the above experiments, it can be seen that on the time axis, when the speed and heat consumption are greater, it will be more difficult to capture the feature vector in sports, to increase the requirements for data processing. Therefore, the model proposed in this article sets an error analysis module on this basis, and the average error is controlled by the basic logic of the algorithm and the filtering process of the neural network. Therefore, it is convenient to achieve the accurate management of visualization. Figures6 and 7 show the analysis of the mean square error and average error of the models involved in this article.Figure 6
Mean square error analysis of the model.Figure 7
Average error analysis of the model.The model and algorithm proposed in this article laugh at data analysis and visual processing errors to a large extent. The model proposed in this article has more matching and superiority when it comes to the complexity and variability of sports. The system’s error rate has been reduced by more than 86%, and it can be improved in some situations. It is difficult to describe the very wooden motion characteristics of a moving object solely using the interval-free density of a single trajectory motion descriptor. The findings show that a wide range of motion patterns and features can be quantified and qualitatively described. The proposed algorithm’s density volumes successfully capture the spatial variation in sports usage and visually identify specific spatial patterns of sports.


## 5. Conclusions

This article makes relevant research on sports visual analysis due to the rapid development of big data technology and its mature application in various fields. Sports are an important part of people’s lives and social development today. And because the country actively promotes the development of sports, people’s interest in competitive sports and viewing habits have improved and changed dramatically. It is essential for data visualization, and the opening and monitoring of data sources are the foundations of data news production. Data collectors follow up and collect visual information on the spot in the traditional visual collection of sports. The core of visualization changes from text to data in the era of big data, and data runs through the entire visualization process to transform the data set and visualize it. From the brief description of data visualization, it can be seen that data visualization is inseparable from computer technology. Therefore, big data technology has more advantages in data visualization than traditional data processing technology. Combined with the research content and experimental test results, the motion visualization system based on the motion recognition algorithm designed in this article can assist in the visual management of sports. In terms of the design algorithm, the three-dimensional space design proposed in this article can improve the efficiency of the motion visualization effect, and also simplifies the calculation and optimization processing in terms of errors. Compared with different dimensions, the algorithm in this article can reduce the error rate to 86%. Above, this will ensure that the system can obtain reliable and reasonable data conclusions in the process of intelligent processing of big data, which has a positive effect on the visualization of our sports management.



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*Source: 2834226-2022-06-30.xml*