This paper presents a method for joint detection and tracking of vehicles with a scanning laser rangefinder. The lidar measurements of an object have the particularity to be spatially distributed, which generally leads to a detection step before any tracking. Differently, the proposed method relies on the raw measurement processing without any detection step, which improves the overall performance in multiobject tracking while providing good estimation accuracies. The solution uses the sequential Monte Carlo methods by incorporating the geometric invariant of the objects of interest (vehicles). This approach also offers an efficient solution to the problem of multitarget tracking by integrating naturally the track management in the filtering process.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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




    Publication date :

    2015




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 Straßenverkehr / 55.24 / 55.84 / 55.24 Fahrzeugführung, Fahrtechnik



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

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


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

    Song, Dan / Tharmarasa, Ratnasingham / Zhao, Weihu et al. | IEEE | 2023


    LiDAR Based Real Time Multiple Vehicle Detection and Tracking

    Zhongzhen Luo / Saeid Habibi / Martin v. Mohrenschildt | BASE | 2016

    Free access

    LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing

    Cellina, Marcello / Corno, Matteo / Savaresi, Sergio Matteo | ArXiv | 2025

    Free access

    Multi-Sensor Joint Detection and Tracking with the Bernoulli Filter

    Ba Tuong Vo / Chong Meng See / Ning Ma et al. | IEEE | 2012