Improved Real-Time Visual Tracking Via Adversarial Learning

The high accuracy and fast running speed are two goals of tracking algorithms. In recent years, many trackers based on deep learning have improved in both aspects respectively, but it is difficult to balance these two indicators. For example, a real-time tracking algorithm named RT-MDNet has greatly increased speed on the MDNet, but the accuracy is still limited to some extent. On the contrary, a state-of-the-art visual tracker based on adversarial learning named VITAL achieves a significant improvement in performance by alleviating the imbalance between positive and negative samples of tracking data. However, its running speed is seriously limited. In this paper, we attempt to combine the advantages from both methods and propose an improved real-time visual tracking algorithm via adversarial learning to get a more balanced result in accuracy and tracking speed. Specifically, we base on the framework of RT-MDNet and introduce a random feature map masking with adversarial learning to improve the quality of feature maps. Experiments on OTB2015 show our algorithm runs at 17 FPS with a precision of 87.6%.