ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information
Multiple-object tracking (MOT) is a crucial component in autonomous driving systems. However, inaccurate object detection is always the bottleneck for MOT. Most detectors are not designed to take the temporal information across consecutive frames into consideration. To take advantage of such information, we design a novel data representation, the spatio-temporal (ST) map, which collects a batch of detection results spatio-temporally, and we train a novel network, ST-TrackNet, to assign predicted track IDs to each positive detection across a sequence. With our ST map detection fed into the tracker, the correlation of objects between adjacent frames becomes prominent, which improves the performance of the tracker in the data association step. Moreover, the long-term trajectory in a sequence also helps to refine the detection results. We train and evaluate our network on the KITTI dataset, a CARLA simulation dataset, and a dataset recorded in a factory environment. Our approach generally achieves superior performance over the state-of-the-art.
|EI Accession Number|
Autonomous vehicles ; Deep learning ; Motion planning ; Object detection ; Object recognition ; Signal to noise ratio ; Tracking (position) ; Trajectories
|ESI Classification Code|
Highway Transportation:432 ; Ergonomics and Human Factors Engineering:461.4 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Robot Applications:731.6
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Mechanical and Energy Engineering|
1.Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
2.Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
3.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
4.The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, China
Wang，Sukai,Sun，Yuxiang,Wang，Zheng,et al. ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information[J]. IEEE Transactions on Automation Science and Engineering,2022,PP(99):1-12.
Wang，Sukai,Sun，Yuxiang,Wang，Zheng,&Liu，Ming.(2022).ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information.IEEE Transactions on Automation Science and Engineering,PP(99),1-12.
Wang，Sukai,et al."ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information".IEEE Transactions on Automation Science and Engineering PP.99(2022):1-12.
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.