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This paper addresses fully automated multi-person tracking in complexenvironments with challenging occlusion and extensive pose variations. Oursolution combines multiple detectors for a set of different regions of interest(e.g., full-body and head) for multi-person tracking. The use of multipledetectors leads to fewer miss detections as it is able to exploit thecomplementary strengths of the individual detectors. While the number of falsepositives may increase with the increased number of bounding boxes detectedfrom multiple detectors, we propose to group the detection outputs by boundingbox location and depth information. For robustness to significant posevariations, deformable spatial relationship between detectors are learnt in ourmulti-person tracking system. On RGBD data from a live Intensive Care Unit(ICU), we show that the proposed method significantly improves multi-persontracking performance over state-of-the-art methods.

Deformable Distributed Multiple Detector Fusion for Multi-Person Tracking