In this study, we introduce a vehicle tracking system for drone imagery, utilizing the real-time object detection network YOLOv5 to get vehicle location and cropped images. The system analyzes the segmented regions' histograms, compares them with previous frames, and identifies identical objects for tracking. The algorithm is designed to compare objects within a specific radius using coordinate information, enhancing histogram comparison efficiency. The MOTA (Multi-Object Tracking Accuracy) showed 90%, but the limited environment of data usage and experiments must be considered. The findings suggest that the real-time performance of the vehicle tracking system can be applied in various fields such as traffic control, vehicle management, and accident response.


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    Titel :

    Vehicle Tracking System in Drone Imagery with YOLOv5 and Histogram


    Beteiligte:
    Choi, Jehwan (Autor:in) / Ha, Seongbo (Autor:in) / Lee, Youlkyeong (Autor:in) / Jo, Kanghyun (Autor:in)


    Erscheinungsdatum :

    09.08.2023


    Format / Umfang :

    1902008 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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