SIAM-REID: confuser aware Siamese tracker with re-identification feature

Abu Md Niamul Taufique; Andreas Savakis; Michael Braun; Daniel Kubacki; Ethan Dell; Lei Qian; Sean O'Rourke

doi:10.1117/12.2594822

1 August 2021 SIAM-REID: confuser aware Siamese tracker with re-identification feature

Abu Md Niamul Taufique, Andreas Savakis, Michael Braun, Daniel Kubacki, Ethan Dell, Lei Qian, Sean O'Rourke

Proceedings Volume 11843, Applications of Machine Learning 2021; 1184315 (2021) https://doi.org/10.1117/12.2594822
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Conference Poster

Abstract

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.

Citation Download Citation

Abu Md Niamul Taufique, Andreas Savakis, Michael Braun, Daniel Kubacki, Ethan Dell, Lei Qian, and 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

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