In the substation scene with many occlusions, the traditional target tracking algorithm is prone to lose the figure, change identity, fail to recognize the target when it is shielded, and cannot achieve accurate real-time tracking. To address this problem, we proposed a sub-station operation target tracking method that combines metric learning and Kalman filtering. Firstly, the YOLOv3 algorithm with multi-scale features is used to detect the target. The Yolov3 target detection algorithm has high detection speed and strong real-time performance. On the basis of Yolov3 algorithm, the Kalman filter is introduced to predict the trajectory, and the metric learning method is used to fit the trajectory, and combined with the appearance of the target, to realize the tracking of the moving target. Finally, the experiment was carried out on the staff in the field. The tracking of moving target can achieve high accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Kalman filter-based Detection and Tracking Method for Substation Moving Targets


    Beteiligte:
    Shen, Huaqiang (Autor:in) / Cheng, Song (Autor:in) / Wang, Yafeng (Autor:in) / Zhang, Yi (Autor:in)


    Erscheinungsdatum :

    12.10.2022


    Format / Umfang :

    1753860 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Moving Vehicle Tracking Based on Kalman Filter

    Yan, Yan ;Shi, Yan Cong ;Ma, Zeng Qiang | Trans Tech Publications | 2011




    Application of the Kalman-Levy Filter for Tracking Maneuvering Targets

    Sinha, A. / Kirubarajan, T. / Bar-Shalom, Y. | IEEE | 2007