In this article, the problem of tracking vehicles using lidar sensors mounted on an ego-vehicle is addressed. Due to line-of-sight limitations, the back (or front) of a vehicle as seen by the lidar on the ego-vehicle behind (or ahead of) it is often modeled as L-shaped. In this article, an L-shape-based vehicle tracking algorithm with joint kinematic and geometric estimation is presented. By feeding back tracking results to L-shape fitting, an L-shape detection method that is robust to outliers is proposed. In the L-shape tracker currently available in the literature, the kinematic and geometric states of the L-shape model are separately estimated and maintained. However, the kinematic and geometric states are not independent since the orientation of a vehicle influences its velocity. Also, the dependence between the kinematic and the geometric states is caused by anchor-point (the closest point on the vehicle being tracked) switching, which is required during changes in the relative position between vehicles. To address this limitation, the proposed L-shape tracker exploits this dependence and estimates the kinematic and geometric states jointly. The proposed L-shape-model-based tracking algorithm is evaluated and compared with the original algorithm using the real traffic data from the KITTI datasets. The results demonstrate the superiority of the proposed algorithm over the original algorithm in terms of L-shape detection and tracking accuracies.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    L-Shape-Model-Based Vehicle Tracking With Joint Kinematic and Geometric Estimation Using Lidar


    Contributors:


    Publication date :

    2023-10-01


    Size :

    1573214 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    L-Shape Fitting-Based Vehicle Pose Estimation and Tracking Using 3D-LiDAR

    Zhao, Chengfeng / Fu, Chen / Dolan, John et al. | IEEE | 2021


    LiDAR-Based Object Tracking and Shape Estimation

    Krämer, Stefan | TIBKAT | 2021

    Free access


    A Model-Based Joint Detection and Tracking Approach for Multi-Vehicle Tracking With Lidar Sensor

    Fortin, Benoit / Lherbier, Regis / Noyer, Jean-Charles | IEEE | 2015