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Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling

Stadler, Daniel; Beyerer, Jürgen

Abstract (englisch):

Multi-pedestrian trackers perform well when targets are clearly visible making the association task quite easy. However, when heavy occlusions are present, a mechanism to reidentify persons is needed. The common approach is to extract visual features from new detections and compare them with the features of previously found tracks. Since those detections can have substantial overlaps with nearby targets - especially in crowded scenarios - the extracted features are insufficient for a reliable re-identification. In contrast, we propose a novel occlusion handling strategy that explicitly models the relation between occluding and occluded tracks outperforming the feature-based approach, while not depending on a separate re-identification network. Furthermore, we improve the track management of a regression-based method in order to bypass missing detections and to deal with tracks leaving the scene at the border of the image. Finally, we apply our tracker in both temporal directions and merge tracklets belonging to the same target, which further enhances the performance. We demonstrate the effectiveness of our tracking components with ablative experiments and surpass the state-of-the-art methods on the three popular pedestrian tracking benchmarks MOT16, MOT17, and MOT20.

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator KITopen-ID: 1000134191
Erschienen in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Veranstaltung IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Online, 19.06.2021 – 25.06.2021
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 10958-10967
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