# A Deep Learning and Computer Vision Based Multi-Player Tracker for Squash
###### Abstract
Another system applied to handball was discussed by [PERSON] et al. [15] who proposed a player tracking method based on image and colors processing. Both methods require specific equipment, a special camera setup and present tradeoffs between manual intervention and analysis times. Specifically for squash, a computer vision driven tracking system was developed using the HOG (Histogram of Oriented Gradients) algorithm implemented within the OpenCV library for player detection [16]. In this method, detection is of the general player rather than definitive tracking of the feet causing valuable kinetic information to be lost. In addition, it requires a special camera setup with prior court calibration and it is noted that the algorithm suffers from poor response time. Few papers study player kinematics and kinetics of squash. In past studies, [PERSON] and [PERSON] [17] investigated the correlation of velocity and acceleration in the last ten seconds of a rally for winners and losers. To collect positional data of players, observers manually annotated video images of squash matches using a digitizing pad and stylus [18; 19]. [PERSON] et al. [20] built on previous work to analyze entire match play as well as at the rally level, studying the distinction between winner and losers of the rally in terms of court position, total distance, average velocity, and acceleration. In a following study, a correlation was drawn to the use of the 'T' area to player ability [21]. These studies utilized the SAGIT/Squash tracking system, a real-time data acquisition tracking system which requires colored images from a birds-eye camera view positioned above the court and compares to an empty court image to determine player position [22; 23]. Correlations have further been studied between the game outcome and rank of elite squash player by quantifying distance traveled, position relative to the T, dominance of the T, average velocities, and frequency distribution of velocities of different ranking players [24]. [PERSON] et al. [24] concluded that total distance and average velocity of the player is not suggestive of the rank of the player or the outcome of the game, however, the player's rank can indicate their ability to dominate the T and control the court. The data were collected using broadcast videos provided by the Professional Squash Association (PSA), analyzing only active match play of the full court view provided by the main camera. Using video analysis software, Dartfish Team Pro version 8 (2015), markers were manually placed on each foot for every eligible frame to determine player position in the video coordinate system. Ten reference points on the court were recorded to determine a coordinate transformation converting the video image coordinate system to the coordinate system of the plane of the court [24]. The contribution of the current paper is to advance the development of accurate and reliable markerless motion tracking for squash by removing the need for a special setup, reducing processing times, and limiting user intervention used by previous squash studies. The proposed methodology improves on the previous work done by our research group [24] by replacing the time-consuming and laborious task of player tracking with an autonomous deep learning based human pose estimation to detect individuals in the frame and computer vision to identify the players. Removing the need for specific equipment and limiting significant user intervention increases the number of eligible matches we can analyze in a timely manner. Matches that are filmed by the PSA or filmed similarly are available to be analyzed by our methodology. This study outlines and validates our proposed method with the results of the previous study completed by [PERSON] et al. [24] that quantify the players' distance traveled, position relative to the T, and average velocities. This is the first study to apply deep learning and computer vision motion tracking techniques to study elite squash players in competition.