Abu Md Niamul Taufique,1 Andreas Savakishttps://orcid.org/0000-0002-9657-3027,1 Michael Braun,2 Daniel Kubacki,2 Ethan Dell,2 Lei Qian,3 Sean O'Rourke3
1Rochester Institute of Technology (United States) 2Systems & Technology Research (United States) 3Air Force Research Lab. (United States)
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Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are prevalent in aerial imagery and create challenging conditions due to prolonged occlusions where the tracker object re-appears under different pose and illumination. Our work proposes SiamReID, a novel re-identification framework for Siamese trackers, that incorporates confuser rejection during prolonged occlusions and is wellsuited for aerial tracking. The re-identification feature is trained using both triplet loss and a class balanced loss. Our approach achieves state-of-the-art performance in the UAVDT single object tracking benchmark.
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Abu Md Niamul Taufique, Andreas Savakis, Michael Braun, Daniel Kubacki, Ethan Dell, Lei Qian, Sean O'Rourke, "SIAM-REID: confuser aware Siamese tracker with re-identification feature," Proc. SPIE 11843, Applications of Machine Learning 2021, 1184315 (1 August 2021); https://doi.org/10.1117/12.2594822