On the Performance of Crowd-Specific Detectors in Multi-Pedestrian Tracking
In recent years, several methods and datasets have been proposed to push the performance of pedestrian detection in crowded scenarios. In this study, three crowd-specific detectors are combined with a general tracking-by-detection approach to evaluate their applicability in multi-pedestrian tracking. Investigating the relation between detection and tracking accuracy, we make the interesting observation that in spite of a high detection capability, the performance in tracking can be poor and analyze the reasons behind that. However, one of the examined approaches can significantly boost the tracking performance on two benchmarks under different training configurations. It is shown that combining crowd-specific detectors with a simple tracking pipeline can achieve promising results, especially in challenging scenes with heavy occlusion. Although our tracker only relies on motion cues and no visual information is considered, applying the strong detections from the crowd-specific model, state-of-the-art results on the challenging MOT17 and MOT20 benchmarks are obtained.