Consensus-based trajectory estimation for ball detection in calibrated cameras systems

This paper considers the detection of the ball in team sport scenes observed with still or motion-compensated calibrated cameras. Foreground masks do provide primary cues to identify circular moving objects in the scene, but are shown to be too noisy to achieve reliable detections of weakly contrasted balls, especially when a single viewpoint is available, as often desired for reduced deployment cost. In those cases, trajectory analysis has been shown to provide valuable complementary information to differentiate true and false positives among the candidates detected by the foreground mask(s). In this paper, we focus on the detection of ball trajectory segments, exclusively from visual cues, without considering semantic reasoning about team play to connect those segments into long trajectories. We revisit several recent works, mainly the ones that we presented in Amit Kumar et al. (ICDSC, 1), Parisot and De Vleeschouwer (ICME, 2), and introduce a publicly available dataset to compare them. We conclude that randomized consensus-based methods are competitive compared to the alternative deterministic graph-based solutions, while offering the additional advantage to naturally extend to the cost-effective single-view scenario. As an original contribution, we also introduce a procedure to efficiently clean up the foreground mask in correlation-based methods like (Amit Kumar et al. in ICDSC, 1) and a nonlinear rank-order filter to merge the foreground cues from multiple viewpoints. We also derive recommendations regarding the camera positioning and the buffering needs of a real-time acquisition system.