Occlusion Free Multi-Object Tracking Extention using Multi-Camera for Sport

Momoe Yamane, Akimasa Kondo, Masashi Hatano, Ryosuke Hori, Hideo Saito

Abstract: As data analysis becomes increasingly significant, streamlining data collection is essential, particularly in sports. Among various aspects, player performance analysis is crucial for both professional and amateur levels. In computer vision, Multi-Object Tracking (MOT) and Multi-Camera Multi-Object Tracking (MCMOT) enable the analysis of people's trajectories captured in videos. However, many existing methods do not account for crowded scenarios, such as sports matches, and require complex preprocessing of datasets, making them impractical for real-world applications. To address these challenges, we introduce a simple yet novel extension of MOT methods by utilizing the Gaussian distribution to associate tracking information obtained from multiple cameras. By integrating tracking data from all cameras, our methodology effectively mitigates identity switches and expands the tracking range. Experimental results demonstrate our tracking system's effectiveness, showing a reduction in ID switches and false negatives compared to results achieved with a standard multi-object tracker.