# A Deep Learning and Computer Vision Based Multi-Player Tracker for Squash
## 4 Discussion  
This study aims to apply deep learning and computer vision processes to evaluate kinematics of elite squash players for the first time. The method was validated when compared with previous results from a manual tracking study [24]. Our method presents many advantages to prior data acquisition methods. The ability to analyze any matches filmed by the PSA or suitable matches filmed from a similar angle, requiring no special camera setup or wearable markers that could impede player movement, significantly increases the number of elite matches eligible for analysis. A notable advancement in the present study is the speed of player tracking, which has been considerably accelerated to 0.3 s per frame. Player statistics are rapidly generated using Python code for easy computation. Thus, an ideal full match analysis takes approximately 3 h including tracking and analysis, where the majority of the process is autonomous. Presently, manual intervention is only required during pre-processing to identify active play. Broadcasts of professional squash matches do not have a definitive visual or auditory indicator of when a rally begins or ends, unlike other racquet sports. Based on our preliminary investigation, some strategies that could be implemented in the future to address this include tracking when the scoreboard changes, noting a change of the camera angle or pan away from the court (note that these implementations will not be completely instantaneous). For the match analyzed, active play was slightly higher than half the total time of each game (55.9% on average). This supports the interpretation of squash being a sport demanding of short, high intensity bursts rather than endurance and constant intensity [31]. Other camera angles such as the sidewall and close up secondary cameras do not display both players and are typically used for repetitive shots, usually drop shots or backhands down the wall from the left back corner. However, the movement of the players were cyclical between the T and the corner and deemed to be relatively equal, providing valid results for comparison and aligning with previous studies [17; 20; 21; 24]. Current work is being done to implement autonomous collection of other frame angles of the match. Future work can be done establishing court conversion matrices using the inverse perspective mapping method used on the main camera angle and other camera angles to analyze the full length of active match play [32; 33]. Frames analyzed by the manual tracking method were used with the proposed tracking method with an average of 83.82% of the input frames per game, resulting in detection and identification of both players. Frames where the system was unable to successfully detect players were due to player occlusion or unnatural pose as mentioned previously in the methods section. A global timestamp was assigned and detected in each frame to account for difference in time when calculating for velocity between missing frames. Comparison of time series dependent results such as player velocities provide support for the effectiveness of this approach. Court conversions were determined using reference points in the frame specific to the court and camera angle. The equations have been noted to be more accurate in predicting a player's position