TY - GEN
T1 - Deep kalman filter with optical flow for multiple object tracking
AU - Chen, Yaran
AU - Zhao, Dongbin
AU - Li, Haoran
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Deep matching and Kalman filter-based multiple 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 DKF1ow-tracking, to improve tracking performance. In DK-flowtracking, optical flow in consecutive frames is used to provide accurate object motion information for guiding Kalman filter 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.
AB - Deep matching and Kalman filter-based multiple 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 DKF1ow-tracking, to improve tracking performance. In DK-flowtracking, optical flow in consecutive frames is used to provide accurate object motion information for guiding Kalman filter 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.
UR - http://www.scopus.com/inward/record.url?scp=85076735690&partnerID=8YFLogxK
U2 - 10.1109/SMC.2019.8914078
DO - 10.1109/SMC.2019.8914078
M3 - Conference Proceeding
AN - SCOPUS:85076735690
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3036
EP - 3041
BT - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
Y2 - 6 October 2019 through 9 October 2019
ER -