Enhancing Detection Model for Multiple Hypothesis Tracking

Jiahui Chen, Hao Sheng, Yang Zhang, Zhang Xiong; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 18-27

Tracking-by-detection has become a popular tracking paradigm in recent years. Due to the fact that detections within this framework are regarded as points in the tracking process, it brings data association ambiguities, especially in crowded scenarios. To cope with this issue, we extended the multiple hypothesis tracking approach by incorporating a novel enhancing detection model that included detection-scene analysis and detection-detection analysis; the former models the scene by using dense confidential detections and handles false trajectories, while the latter estimates the correlations between individual detections and improves the ability to deal with close object hypotheses in crowded scenarios. Our approach was tested on the MOT16 benchmark and achieved competitive results with current state-of-the-art trackers.