# A Comprehensive Review of Computer Vision in Sports:
## 1 Introduction  
Automatic analysis of video in sports is a possible solution to the demands of fans and professionals for various kinds of information. Analyzing videos in sports has provided a wide range of applications, which include player positions, extraction of the ball's trajectory, content extraction, and indexing, summarization, detection of highlights, on-demand 3D reconstruction, animations, generation of virtual view, editorial content creation, virtual content insertion, visualization and enhancement of content, gameplay analysis and evaluations, identifying player's actions, referee decisions and other fundamental elements required for the analysis of a game. The task of player detection (identification) and tracking is very difficult because of many challenges, which include the similar appearance of subjects, complex occlusions, an unconstrained field environment, background, unpredictable movements, unstable camera motion, issues with calibration of low textured fields, and the editing performed for broadcasting video, lower pixel resolution of players who are distant and smaller in the frame, and motion blur, among others. The simultaneous detection of players and ball and tracking them at once is quite challenging, because of the zigzag movements of the ball and player, change of the ball from player to player, severe occlusion between players and the ball. Hence, this paper presents a survey of detection, classification, tracking, trajectory prediction and recognizing the team's strategies in various sports. Detection and tracking of players is the only major requirement in some sports such as cycling and swimming. Hence,this study presents a survey of detection, classification, tracking, trajectory prediction and recognizing the team's strategies in various sports. Detection and tracking of the player is the only major requirement in some sports such as cycling, swimming, among others. As a result, as illustrated in Figure 1, this research classifies all sports into two categories: player-centered and ball-centered sports, with extensive analysis in Section 4. Recent developments in video analysis of sports have a focus on the features of computer vision techniques, which are used to perform certain operations for which these are assigned, such as detailed complex analysis such as detection and classification of each player based on their team in every frame or by recognizing the jersey number to classify players based on their team will help to classify various events where the player is involved. In higher-level analysis, such as tracking the player or ball, many more such evaluations are to be considered for the evaluation of a player's skills, detecting the team's strategies, events and the formation of tactical positions such as midfield analysis in various sports such as soccer, basketball, and also various sports vision applications such as smart assistants, virtual umpires, assistance coaches, have been discussed in Section 7. A higher-level semantic interpretation is an effective substitute, especially in situations when reduced human intervention and real-time analysis are desired for the exploitation of the delivered system outputs. The main task of video summarization or highlight extraction is extracting key events of the game which provides users with an ability to view highlights as per their interests. For this purpose, it is necessary to detect and classify gestures, recognize the actions of the referee/umpire, track players and the ball in key events like the time of goal scoring to analyze and classify different types of shots performed by players. The framework for processing and analyzing task-specific events in sports applications, such as playfield extraction, detection, and tracking of the player/ball is shown in Figure 2, and detailed analyses of playfield extraction are discussed in Section 3.