TY - GEN
T1 - Multi-3D-Object Tracking by Fusing RGB and 3D-LiDAR Data
AU - Wang, He
AU - Fang, Jianwu
AU - Cui, Saijia
AU - Xu, Hongke
AU - Xue, Jianru
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Multiple object tracking (MOT) is a fundamental problem in the autonomous driving research community. Through accurate and efficient tracking, ego-vehicle can get the location velocity of surrounding objects and make a reasonable future motion planning. Different from most of the methods adopting the RGB or 3D-LiDAR data independently, this paper aims to track the perceived objects by fusing RGB and 3D-LiDAR data, the standard sensor configuration in current autonomous vehicles. Specifically, we firstly use Hungarian algorithm as a backbone model to associate the 3D point cloud of each object in adjacent frames. Then, we fully explore the appearance feature in RGB frame and geometrical feature in 3D point cloud to restrict the wrongly associate target IDs because of the interaction of near objects. We evaluate our method on the newly proposed BLVD dataset, and show the favorable performance.
AB - Multiple object tracking (MOT) is a fundamental problem in the autonomous driving research community. Through accurate and efficient tracking, ego-vehicle can get the location velocity of surrounding objects and make a reasonable future motion planning. Different from most of the methods adopting the RGB or 3D-LiDAR data independently, this paper aims to track the perceived objects by fusing RGB and 3D-LiDAR data, the standard sensor configuration in current autonomous vehicles. Specifically, we firstly use Hungarian algorithm as a backbone model to associate the 3D point cloud of each object in adjacent frames. Then, we fully explore the appearance feature in RGB frame and geometrical feature in 3D point cloud to restrict the wrongly associate target IDs because of the interaction of near objects. We evaluate our method on the newly proposed BLVD dataset, and show the favorable performance.
KW - Driving Scenarios
KW - Hungarian algorithm
KW - Multiple object tracking
KW - Sensor fusion
UR - https://www.scopus.com/pages/publications/85080917548
U2 - 10.1109/ICUS48101.2019.8995984
DO - 10.1109/ICUS48101.2019.8995984
M3 - 会议稿件
AN - SCOPUS:85080917548
T3 - Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
SP - 941
EP - 946
BT - Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
Y2 - 17 October 2019 through 19 October 2019
ER -