  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.
