Fast and robust visual tracking with hard balanced focal loss and guided domain adaption

摘要

Recently, Siamese networks based trackers have shown excellent performance in accuracy and speed. However, previous studies treat all training samples equally and use the general feature space without adapting to the specific video during tracking. These trackers ignore the class data imbalance during training and the feature space difference between the generic domain and the current tracking target domain, which limits the robustness of trackers. In this paper, we propose an algorithm for learning a discriminative and self-adaptive feature representation, in order to achieve accurate and robust tracking. During the off-line training stage, a hard balanced focal loss function is utilized to solve the positive-negative samples imbalance and the hard-easy negative samples imbalance. During the tracking phase, an off-line trained guided domain adaptation module is embedded into the Siamese networks, which can quickly transfer the feature space from the general domain to the current video domain by adjusting the search branch channel weights. Our networks are trained in an end-to-end manner and without online updating. Our tracker runs at 130 FPS while achieving favorable performance against the state-of-the-art methods on OTB-2013, OTB-2015, VOT-2016, VOT-2017, GOT-10 K and TC-128 benchmarks. (C) 2020 Elsevier B.V. All rights reserved.