Towards a better understanding of the overall health impact of the game of squash: Automatic and high-resolution motion analysis from a single camera view

Abstract

In this paper, we present a method for locating and tracking players in the game of squash using Gaussian mixture model background subtraction and agglomerative contour clustering from a calibrated single camera view. Furthermore, we describe a method for player re-identification after near total occlusion, based on stored color- and region-descriptors. For camera calibration, no additional pattern is needed, as the squash court itself can serve as a 3D calibration object. In order to exclude non-rally situations from motion analysis, we further classify each video frame into game phases using a multilayer perceptron. By considering a player’s position as well as the current game phase we are able to visualize player-individual motion patterns expressed as court coverage using pseudo colored heat-maps. In total, we analyzed two matches (six games, 1:28h) of high quality commercial videos used in sports broadcasting and compute high resolution (1cm per pixel) heat-maps. 130184 manually labeled frames (game phases and player identification) show an identification correctness of 79.28±8.99% (mean±std). Game phase classification is correct in 60.87±7.62% and the heat-map visualization correctness is 72.47±7.27%.