# A Comprehensive Review of Computer Vision in Sports:
## 1 Introduction
#### Features of the Proposed Review  
Some of the surveys and reviews published in different sports video processing and their main contributions, i.e., whether the paper discussed (marked as '\(\blacktriangledown\)' if discussed in Table 1) hand-crafted and machine learning algorithms, the type of sport, different tasks in sports, whether it provided datasets or not and finally the aim of the review are discussed and summarized in Table 1 and listed below. * [PERSON] et al. [1] researched badminton movement analysis such as Badminton smashing, badminton service recognition, badminton swing, and shuttle trajectory analysis. * [PERSON] et al. [2] presented a systematic review of sports data mining, which discusses the current research body, themes, the dataset used, algorithms, and research opportunities. * [PERSON] et al. [3] presented a survey on video-based sports intelligence systems used to recognize sports actions. They provided video-based action recognition frameworks used in the sports field and also discussed deep learning implementation in video-based sports action recognition. They proposed a flexible method that classifies actions in different sports with different contexts and features as part of future research. * [PERSON] and [PERSON] [4] presented a summary of 17 human motion capture systems which reports calibration specs as well as the specs provided by the manufacturer. This review helps researchers to select a suitable motion capture system for experimental setups in various sports. * [PERSON] et al. [5] presented a survey on state-of-the-art (SOTA) algorithms for player tracking in soccer videos. They analyzed the strengths and weaknesses of different approaches and presented the evaluation criteria for future research. * [PERSON] [6] proposed an analysis of computer vision-based applications and research topics in the sports field. The study summarized some of the commercially available systems such as camera tracking and player tracking systems. They also incorporated some of the available datasets of different sports. * [PERSON] et al. [7] presented a systematic review on machine learning and deep learning for sports-specific movement recognition using inertial measurement units and computer vision data. Figure 2: Framework of processing and analysis of different applications in sports video. * [PERSON] et al. [8] presented an exhaustive survey on ball tracking categorically and reviewed several used techniques, their performance, advantages, limitations, and their suitability for a different sports. * [PERSON] [9] focused on the content analysis fundamentals (e.g., sports genre classification, the overall status of sports video analytics). Additionally reviewed are SOTA studies with prominent challenges observed in the literature. * [PERSON] et al. [10] explored AI techniques that have been applied to challenges within team sports such as match outcome prediction, tactical decision making, player investments, and injury prediction. * [PERSON] et al. [11] suggested a taxonomy of the existing algorithms and presented a systematic review of the relevant literature that shows the evolution of deep learning-based video summarization technologies. * [PERSON] et al. [12] explored a review to better understand the usage of wearable technology in sports to improve performance and avoid injury. * [PERSON] et al. [13] offered a thorough overview of the literature on the use of wearable inertial sensors for performance measurement in various sports. The proposed survey mainly focuses on providing a proper and comprehensive survey of research carried out in computer vision-based sports video analysis for various applications such as detection and classification of players, tracking players or balls and predicting the trajectories of players or balls, recognizing the team's strategies, classifying various events on the sports field, etc. and in particular, establishing a pathway for next-generation research in the sports domain.