Localization-Aware Meta Tracker Guided With Adversarial Features

Deep learning has recently shown great potentials in learning powerful features for visual tracking. However, most deep learning-based trackers neglect localization accuracy in the evaluation process of candidates. What's more, they usually over-rely on the discriminative features in a single frame in the training process. Consequently, they may fail when the discriminative features are occluded or changed in the tracking phase. In this paper, we propose a novel localization-aware meta tracker (LMT) guided with adversarial features to address the above issues. First of all, we design a novel intersection over union guided method to effectively balance the problem of classification and localization accuracy. To further improve the robustness of our classifier, we creatively use adversarial features during offline training phase. Those adversarial features can effectively guide the classifier in learning how to better deal with the situation where the discriminative features are occluded or changed. Finally, benefiting from meta learning, our algorithm only needs to perform one iterative update on the first frame and it can perform well on the tracking sceneries. The extensive experiments demonstrate the outstanding performance of our LMT compared with the state-of-the-art trackers on three benchmarks: OTB-2015, VOT-2016, and VOT-2018.