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ST-TrackNet: A Multiple-Object Tracking Network Using Spatio-Temporal Information

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  • NETL
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  • IEEE
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. Note to Practitioners—We investigate the MOT problem in this paper. A spatio-temporal pipeline is proposed to provide a solution to this problem. Object detection results produced by off-the-shelf object detectors are used to form the proposed ST maps. In low signal-to-noise ratio (SNR) situations, our proposed framework can achieve more accurate and robust tracking results with more false-positives. Due to the simplicity and modular design of our framework, it can be applied directly after the detection stage to achieve the online tracking task. The proposed method is evaluated on several datasets, and the experimental results demonstrate its effectiveness. Our method can also be used for other autonomous driving applications, such as path planning and trajectory prediction.

TrajectoryFeature extractionTrackingPoint cloud compressionObject detectionDetectorsLong short term memory

Sukai Wang、Yuxiang Sun、Zheng Wang、Ming Liu

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Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China|Clear Water Bay Institute of Autonomous Driving, Nanshan, China

Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, China|Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China|HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, China

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2024

10.1109/TASE.2022.3216450

IEEE transactions on automation science and engineering

IEEE transactions on automation science and engineering

EISCI
ISSN：
年,卷(期)：2024.21(1)
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