MULTIPLE HUMAN TRACKING USING RETINANET FEATURES, SIAMESE NEURAL NETWORK, AND HUNGARIAN ALGORITHM

Multiple human tracking based on object detection has been a challenge due to its complexity. Errors in object detection would be propagated to tracking errors. In this paper, we propose a tracking method that minimizes the error produced by object detector. We use RetinaNet as object detector and Hungarian algorithm for tracking. The cost matrix for Hungarian algorithm is calculated using the RetinaNet features, bounding box center distances, and intersection of unions of bounding boxes. We interpolate the missing detections in the last step. The proposed method yield 43.2 MOTA for MOT16 benchmark.