AFJPDA: a multiclass multi-object tracking with appearance feature-aided joint probabilistic data association - Kim, Sukkeun; Petrunin, Ivan; Shin, Hyo-Sang (American Institute of Aeronautics and Astronautics (AIAA), 2024)
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.
Keywords: Unmanned Aerial Vehicle; Kalman Filter; Image Sensor; Multi-Object Tracking; Joint Probabilistic Data Association