# A Deep Learning and Computer Vision Based Multi-Player Tracker for Squash
###### Abstract  
Sports pose a unique challenge for high-speed, unobtrusive, uninterrupted motion tracking due to speed of movement and player occlusion, especially in the fast and competitive sport of squash. The objective of this study is to use video tracking techniques to quantify kinematics in elite-level squash. With the increasing availability and quality of elite tournament matches filmed for entertainment purposes, a new methodology of multi-player tracking for squash that only requires broadcast video as an input is proposed. This paper introduces and evaluates a markerless motion capture technique using an autonomous deep learning based human pose estimation algorithm and computer vision to detect and identify players. Inverse perspective mapping is utilized to convert pixel coordinates to court coordinates and distance traveled, court position, T' dominance, and average speeds of elite players in squash is determined. The method was validated using results from a previous study using manual tracking where the proposed method (filtered coordinates) displayed an average absolute percent error to the manual approach of 3.73% in total distance traveled, 3.52% and 1.26% in average speeds <9 m/s with and without speeds <1 m/s, respectively. The method has proven to be the most effective in collecting kinematic data of elite players in squash in a timely manner with no special camera setup and limited manual intervention. video tracking; sports broadcast analysis; player identification; kinematics; racquet sports 202020  
without their drawbacks. Manual notation involves replay of game film and the manual localization of joints of interest for each sequential frame and from each camera perspective if required [5]. In the implementation by [PERSON] and [PERSON], notational analysis was used to describe sequential strokes in squash using symbols and court plans for positional information [6]. This method is affordable and does not require attachment of markers but remains a time-consuming and laborious task prone to subjective error. Marker-based systems utilize multiple cameras and markers placed on the specific joints of the subject to locate the position of the body. Many commercially available systems use automatic optoelectronics which require subjects to place passive reflective markers on their body usually in the form of a suit, as reported by [PERSON] who reviewed passive marker systems including the Ariel system, Motion Analysis' HiRes system, Peak Performance's Motus system, Qualisys' ProReflex system, BTS's ElitePlus system, and Vicon's 370 system [7]. The passive markers work with multiple cameras that emit invisible infrared light and reflect the infrared light back to the cameras. The cluster of markers improves time efficiency as it allows for quick location of the subject. With decreased processing time, limitations remain due to the markers which include long participant preparation time and inevitable variability in placement [8]. There is also a higher possibility of rigid body assumptions being violated [9]. Additionally, markers cannot be used in most competition settings due to the physical and/or psychological effects from having extra attachments. Further, because these methods require specialized equipment, data collection is restricted to local participants, limiting the ability to study elite level athletes from across the world. These limitations have motivated the development of motion analysis systems towards an autonomous, markerless approach using deep learning and computer vision. These applications are mostly on slow movements such as walking or jogging and have remained in laboratory analyses as studies evaluate the accuracy of their systems with the use of multiple cameras [10; 11; 12; 13]. More recently, computer vision has been applied to player tracking for indoor sports. [PERSON] and [PERSON] [14] focused on tracking handball players using two cameras and performing applied frames subtraction based on motion detection, template tracking, and color-based tracking.