OccluTrack: Rethinking Awareness of Occlusion for Enhancing Multiple
  Pedestrian Tracking
Jianjun Gao Yi Wang Kim-Hui Yap Kratika Garg Boon Siew Han
Multiple pedestrian tracking faces the challenge of tracking pedestrians in
the presence of occlusion. Existing methods suffer from inaccurate motion
estimation, appearance feature extraction, and association due to occlusion,
leading to inadequate Identification F1-Score (IDF1), excessive ID switches
(IDSw), and insufficient association accuracy and recall (AssA and AssR). We
found that the main reason is abnormal detections caused by partial occlusion.
In this paper, we suggest that the key insight is explicit motion estimation,
reliable appearance features, and fair association in occlusion scenes.
Specifically, we propose an adaptive occlusion-aware multiple pedestrian
tracker, OccluTrack. We first introduce an abnormal motion suppression
mechanism into the Kalman Filter to adaptively detect and suppress outlier
motions caused by partial occlusion. Second, we propose a pose-guided re-ID
module to extract discriminative part features for partially occluded
pedestrians. Last, we design a new occlusion-aware association method towards
fair IoU and appearance embedding distance measurement for occluded
pedestrians. Extensive evaluation results demonstrate that our OccluTrack
outperforms state-of-the-art methods on MOT-Challenge datasets. Particularly,
the improvements on IDF1, IDSw, AssA, and AssR demonstrate the effectiveness of
our OccluTrack on tracking and association performance.