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Delving into monocular 3D vehicle tracking: a decoupled framework and a dedicated metric

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Abstract

Acquiring 3D trajectories of on-road vehicles is an essential visual task for autonomous driving systems. Existing 3D vehicle tracking methods either rely on point cloud data or need to be trained on visual tracking datasets. In comparison, a decoupled monocular 3D vehicle tracking framework is proposed in this paper. Because our framework is the first of its kind, a previous decoupled LiDAR-based method is taken as the baseline by substituting its detector with a monocular one. On this foundation, we further employ global coordinates to cancel out ego motion and introduce the angular rate into the 3D Kalman filter. In order to tackle the problem of long-term association, a trajectory management scheme is proposed with our novel hibernation mechanism. Furthermore, it is pointed out that current monocular 3D tracking methods have not been tailored for the depth estimation uncertainty produced by monocular 3D detectors. In this regard, we propose a depth-aware association strategy which endows remoter vehicles with larger matching regions in the data association stage. As another contribution, we discuss the defects of current metrics for evaluating 3D tracking performance and devise a nonuniform metric which is dedicated to monocular vision. Through extensive experiments conducted on the KITTI tracking benchmark, the superiority of proposed monocular 3D vehicle tracking framework and metric is demonstrated by both quantitative results and qualitative intuition.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant U1964201.

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Authors and Affiliations

  1. The Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, 150001, China

    Tianze Gao, Zhixiang Jia & Weiyang Lin

  2. Ningbo Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, 150001, China

    Zhixiang Jia

  3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Yu Li

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  1. Tianze Gao
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  2. Zhixiang Jia
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  4. Yu Li
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Correspondence to Weiyang Lin.

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Gao, T., Jia, Z., Lin, W. et al. Delving into monocular 3D vehicle tracking: a decoupled framework and a dedicated metric. Appl Intell 53, 746–756 (2023). https://doi.org/10.1007/s10489-022-03432-4

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Keywords

Mathematics Subject Classification (2010)