# A Comprehensive Review of Computer Vision in Sports:
## 9 Conclusions  
Sports video analysis is an emerging and very dynamic field of research. This study comprehensively reviewed sports video analysis for various applications such as tracking players or balls in sports and predicting the trajectories of players or balls, players' skill, and team strategies analysis, detecting and classifying objects in sports. As per the requirements of deploying computer vision techniques in various sports, we provided some of the publicly available datasets related to a particular sport. Detailed discussion on GPU-based work stations, embedded platforms and AI applications in sports are presented. We have present various classical techniques and AI techniques employed in sports, their performance, pros, cons, and suitability to particular sports. We list probable research directions, existing challenges, and current research trends with a brief discussion and also widely used computer vision techniques in various sports. Individual player tracking in sports is very helpful for coaches and personal trainers. Though the sports include particularly challenging tasks such as similarities between players, generation of blurry video segments in some cases, partial or full occlusions between players, the invisibility of jersey numbers in some cases, computer vision is the best possible solution to achieve player tracking. Classification of jersey numbers in sports such as soccer and basketball is quite simple as they have plain jerseys, but in the case of sports such as hockey and American football, the jerseys are massive and come with sharp contours, due to which jersey number recognition is quite hard. By implementing proper bounding box techniques and digit recognition methods, better performance vis-a-vis jersey number recognition in every sport can be achieved. As the appearance of players varies from sport to sport, an algorithm trained on one sport may not work when it is tested on another sport. The problem may be solved by considering a dataset that contains a small set of samples from every sport for fine-tuning. In the case of multi-player tracking in real-time sports videos, severe occlusions cause a critical problem of identity switching among the players. The continuous movement of players makes it difficult to read jersey numbers. A player's similar appearance to another and severe occlusions make it difficult to track and identify players, referees, and goalkeepers reliably. Multiple object tracking in sports is a key prerequisite for the realization of advanced operations in sports, such as player movement and their position in sports, which will give good objective criteria to the team manager for developing a new plan to improve team performance as well as evaluating each player accurately. Commercially used multi-camera tracking systems of players rely on some mixtures of manual and automated tracking and player labeling. Optical tracking systems are a good approach for tracking players occluding each other or players with a similar appearance. The algorithm may detect false positives from out of the court such as fans wearing team uniforms, as the appearance of fans is similar to that of players. This can be eliminated by estimating the play area or broadcast camera parameters with extra spatiotemporal locations of player positions. Action recognition in sports videos is explicitly a non-linearity problem, which can be obtained by aligning feature vectors, by providing a massive amount discriminative video representations to capture the temporal structure of the video that is not present in the dynamic image space and analyzing the salient regions of the frames for action recognition. The algorithms employed so far for detecting and tracking ball movements began with estimating the 3D ball position in trajectory. Employing these methods is very critical, as they include a lot of mathematical relations and require reliable reference objects to construct the path of the trajectory.