Compared to images captured from ground-level perspectives, objects in UAV images are often more challenging to track due to factors such as long-distance shooting, occlusion, and motion blur. Traditional multi-object trackers are not well-suited for UAV multi-object tracking tasks. To address these challenges, we propose an online multi-object tracking network, OMCTrack. To better handle object occlusion and re-identification, we designed an occlusion perception module that re-identifies lost objects and manages occlusion without increasing computational complexity. By employing a simple yet effective hierarchical association method, this module enhances tracking accuracy and robustness under occlusion conditions. Additionally, we developed an adaptive motion compensation module that leverages prior information to dynamically detect image distortion, enabling the system to handle the UAV’s complex movements. The results from the experiments on the VisDrone2019 and UAVDT datasets demonstrate that OMCTrack significantly outperforms existing UAV video tracking methods.


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    Title :

    OMCTrack: Integrating Occlusion Perception and Motion Compensation for UAV Multi-Object Tracking


    Contributors:
    Zhaoyang Dang (author) / Xiaoyong Sun (author) / Bei Sun (author) / Runze Guo (author) / Can Li (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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