| Citation: | HOU Z Q，ZHAO J X，CHEN Y，et al. Cascaded object drift determination network for long-term visual tracking[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2240-2252 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0504
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Aiming at the problems of artificially selecting the threshold and poor determination performance in the existing object drift determination criteria, this paper proposes a cascaded object drift determination network with adaptive threshold selection. Firstly, through the cascade design of the two sub-networks, determine whether the tracking results drift. The results are then jointly determined by the proposed network using the static template, long-term template, and short-term template. A long-term and short-term template update strategy is then designed to guarantee the quality of the template and adapt it to the object’s changing appearance during the determination process. Finally, the proposed network is combined with the short-term tracker TransT and the global re-detection method GlobalTrack to build a long-term tracking algorithm TransT_LT. The proposed algorithm’s performance test on four datasets (UAV20L, LaSOT, VOT2018-LT, and VOT2020-LT) demonstrates that it performs better over the long term in tracking, particularly on the UAV20L dataset, where it outperforms the benchmark algorithm by 7.7% and 10.3%, respectively, in tracking success rate and accuracy. The determination speed of the proposed network is 100 frames per second, which has little effect on the speed of the long-term tracking algorithm.
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