# A Comprehensive Review of Computer Vision in Sports:
## 8 Research Directions in Sports Vision  
Based on the investigation of the available articles in sports, we were able to come out with various research topics and identifies research directions to be taken for further research in sports. They are categorized based on the task specifics in sports applications (such as major sports in which player/ball/referee detection and tracking, pose estimation, trajectory prediction are required) as shown in Figure 18 to provide promising and potential research directions for future computer vision/video processing in sports.  
As sporting activities are dynamic, the accuracy and reliability of single- or multi-player tracking [63; 215] in real-time sports video can be enhanced by proposing a framework that learns object identities with deep representations which resolve the problem of identity switching among players [212]. By considering the temporal information, the performance of the tracking algorithm can become robust to overcome problems such as severe occlusions and miss-detection.  
The accuracy of classifying different defensive strategies of soccer [49] can be improved by labeling large spatio-temporal datasets and by classifying the actions into subtypes [88]. The performance measures of team tactics analysis [93; 95] of soccer videos can be enhanced by analyzing player trajectories. By incorporating the temporal information, the classification accuracy can be improved while it also offers more specific insights into situations such as pass events in the case of non-obvious insights in soccer videos [93]. Accurate pose detection as shown in Figure 19a is still a major challenge to identify whether the player is running, jumping, or walking as shown in Figure 19b, and also to handle the severe occlusions or identity switches among players.  
To assess the batter's caliber, certain aspects of batting need to be considered, i.e., position of batsman before playing a shot, and the method of batting shots for a particular bowling type needs to be modeled [116]. Classification and summarization techniques can minimize false positives and false negatives to detect and classify umpire poses [133]. Detecting various moments such as whether the ball hit the bat and precise detection of the player and wicket keeper at the moment of run-outs, as shown in Figure 20a, is still a  
Figure 18: Major task specifics in sports applications.  
major issue in cricket. Predicting the trajectory of balls bowled by spin bowlers as shown in Figure 20b can be resolved accurately by labeling large datasets and modeling using SOTA algorithms.  
The recognition accuracy of player actions in badminton games [191] can be improved by SOTA computer vision algorithms and fine-tuning in an end-to-end manner with a larger dataset on features extracted at different fully connected layers. In the implementation of an automatic linesman system in badminton games [295], the algorithm is not robust to the far views of the camera, where illumination conditions heavily impact the system while the speed of the shuttlecock is also a major factor for poor accuracy. So, it is necessary to track the path, which becomes simpler for the referee to decide if a shuttle lands out or in as shown in Figure 21.  
Figure 19: Instances from soccer matches. (**a**) Detecting body pose and limbs. (**b**) Handling severe occlusions among players.  
Figure 21: Exact spot on which the shuttlecock lands.  
Figure 20: Instances from cricket matches. (**a**) Precise detection at the moment of run outs. (**b**) Predicting the trajectory of the ball in-line or out-line, etc.