This study addresses a multiclass multi-object tracking problem in consideration of clutters in the environment. To alleviate issues with clutters, we propose the appearance feature-aided joint probabilistic data association filter. We also implemented simple adaptive gating logic for the computational efficiency and track maintenance logic, which can save the lost track for re-association after occlusion or missed detection. The performance of the proposed algorithm was evaluated against a state-of-the-art multi-object tracking algorithm using both multiclass multi-object simulation and real-world aerial images. The evaluation results indicate significant performance improvement of the proposed method against the benchmark state-of-the-art algorithm, especially in terms of reduction in identity switches and fragmentation.


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    Title :

    AFJPDA: A Multiclass Multi-Object Tracking with Appearance Feature-Aided Joint Probabilistic Data Association


    Contributors:


    Publication date :

    2023-12-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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