Kim, SukkeunPetrunin, IvanShin, Hyo-Sang2024-03-142024-03-142024-01-02Kim S, Petrunin I, Shin HS. (2024) AFJPDA: a multiclass multi-object tracking with appearance feature-aided joint probabilistic data association. Journal of Aerospace Information Systems, Volume 21, Issue 4, April 2024, pp. 294-304https://doi.org/10.2514/1.I011301https://dspace.lib.cranfield.ac.uk/handle/1826/20997This 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Unmanned Aerial VehicleKalman FilterImage SensorMulti-Object TrackingJoint Probabilistic Data AssociationArticle2327-3097