Parisot, Pascaline
[UCL]
De Vleeschouwer, Christophe
[UCL]
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, 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 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.
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Bibliographic reference |
Parisot, Pascaline ; De Vleeschouwer, Christophe. Consensus-based trajectory estimation for ball detection in calibrated cameras systems. In: Journal of Real-Time Image Processing, Vol. 2016, no.Sept, p. 1-16 (September 2016) |
Permanent URL |
http://hdl.handle.net/2078.1/177090 |