Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
Occlusion-robust tracker combining YOLOv11n, Kalman filtering, and appearance Re-ID cuts identity switches and beats OccluTrack by 18.1% MOTA on OVIS.
The framework integrates YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification to maintain target identity through full and long-term occlusion. Six Re-ID architectures were evaluated under identical conditions, with the Occlusion-Aware Mask Network (OAMN) performing best. On the public OVIS dataset it improves MOTA by 18.1% and IDF1 by 25.1% over OccluTrack while reducing identity switches by 12.8%; on a custom military surveillance dataset it achieves MOTA 0.734 and IDF1 0.729.
- Combines YOLOv11n detection, Kalman prediction, and occlusion-aware appearance Re-ID.
- Occlusion-Aware Mask Network selected as best of six evaluated Re-ID architectures.
- OVIS: +18.1% MOTA, +25.1% IDF1 over OccluTrack; identity switches down 12.8%.
- Custom military dataset: MOTA 0.734, IDF1 0.729 under long-term occlusion.
From the original article
1 min read at arxiv.orgReal-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear from the camera's field of view. Conventional trackers may terminate trajectories prematurely, resulting in identity loss and reduced situational awareness in applications such as defense and surveillance.
By Mais Mohammed · arxiv.org
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