中国科学院自动化研究所机构知识库

Deep matching and Kalman filter-based multi- ple object tracking (DK-tracking) have been demonstrated to be promising. However, most of existing DK-tracking trackers assume that objects are slow-varying movement with a constant velocity. The assumption is hard to be satisfied in the real world, especially in the image space due to the sight distance. In this paper, we propose a novel multiple object tracking method combining deep feature matching, Kalman filter and flow information, which is called DK- Flow-tracking, to improve tracking performance. In DK-flow- tracking, optical flow in consecutive frames is used to provide accurate object motion information for guiding Kalman fil- ter to track objects. Experiments are performed on public datasets: MOT2016, MOT2017, and the proposed method achieves better performances compared to the DK-tracking with the assumption of a constant velocity movement.