Deformable Distributed Multiple Detector Fusion for Multi-Person Tracking
This work addresses multi-person tracking in challenging settings like ICUs, offering an incremental improvement over existing methods.
The paper tackles multi-person tracking in complex environments with occlusion and pose variations by combining multiple detectors for different body regions and grouping detections using location and depth, achieving significant performance improvements over state-of-the-art methods on ICU RGBD data.
This paper addresses fully automated multi-person tracking in complex environments with challenging occlusion and extensive pose variations. Our solution combines multiple detectors for a set of different regions of interest (e.g., full-body and head) for multi-person tracking. The use of multiple detectors leads to fewer miss detections as it is able to exploit the complementary strengths of the individual detectors. While the number of false positives may increase with the increased number of bounding boxes detected from multiple detectors, we propose to group the detection outputs by bounding box location and depth information. For robustness to significant pose variations, deformable spatial relationship between detectors are learnt in our multi-person tracking system. On RGBD data from a live Intensive Care Unit (ICU), we show that the proposed method significantly improves multi-person tracking performance over state-of-the-art methods.