# A Comprehensive Review of Computer Vision in Sports:
## 9 Conclusions
Kalman filter- and particle filter-based methods are robust concerning the size, shape, and velocity of the ball. However, the methods fail to establish the track when the ball reappears after occlusion. Trajectory-based methods solve the problem of occlusion and are robust in obtaining data regarding missing and merging balls but fail to obtain data regarding the case of the size and shape of the ball. Data association methods are best suited for detecting and tracking small size balls in small courts such as tennis courts but are not suited for challenges in sports such as basketball, soccer, and volleyball. AI algorithms predict the precise trajectories of the ball from a knowledge of previous frames and are immune to challenges such as air friction, ball spin, and other complex ball movements. A precise database that includes different sizes and shapes of the ball has to be introduced to detect the ball position and enable tracking algorithms to perform efficiently. Detection and tracking of players, balls and assistant referee as well as semantic scene understanding in computer vision applications of sports is still an open research area due to various challenges such as sudden and rapid changes in movements of the players and ball, similar appearance, players with extreme aspect ratios (players have extremely small aspect ratios in terms of height and width when they fall down on the field) and frequent occlusions. The future scope of computer vision research in sports, therefore, is handling limitations more accurately on different AI algorithms. As the betting process involves financial assets, it is important to decide which team is likely to win; therefore, bookmakers, fans, and potential bidders are all interested in estimating the odds of the game in advance. So, provisional tactical analysis of field sports related to player formation in sports such as soccer, basketball, rugby, American football, and hockey, as well as pass prediction, shot prediction, and expectations of goals in a given game state or a possession, or more general game strategies, need to be analyzed in advance. Tracking algorithms that are used in various sports cannot be compared on a common scale as experiments, requirements, situations, and infrastructure in every scenario differ. Determining the performance benchmark of algorithms quantitatively is quite difficult due to the unavailability of a comparable database with ground truths of different sports differing in many aspects. In addition to these, there are additional parameters such as different video capturing devices and their parameter variations which lead to difficulty in building an object tracking system in the sports field. Conceptualization, B.T.N. and M.F.H. ; methodology, B.T.N. and M.F.H. ; software, B.T.N. ; validation, B.T.N., M.F.H. and N.D.B. ; formal analysis, B.T.N. and M.F.H. ; investigation, B.T.N., M.F.H. and N.D.B. ; resources, M.F.H. and N.D.B. ; data curation, B.T.N. and M.F.H. ; writing--original draft preparation, B.T.N., M.F.H. and N.D.B. ; writing--review and editing, B.T.N., M.F.H. and N.D.B. ; visualization, B.T.N. and N.D.B. ; supervision, M.F.H. and N.D.B. ; project administration, M.F.H. and N.D.B. ; funding acquisition, M.F.H. and N.D.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.