GARAT: Generative Adversarial Learning for Robust and Accurate Tracking.

Object tracking by the Siamese network has gained its popularity for its outstanding performance and considerable potential. However, most of the existing Siamese architectures are faced with great difficulties when it comes to the scenes where the target is going through dramatic shape or environmental changes. In this work, we proposed a novel and concise generative adversarial learning method to solve the problem especially when the target is going under drastic changes of appearance, illumination variations and background clutters. We consider the above situations as distractors for tracking and joint a distractor generator into the traditional Siamese network. The component can simulate these distractors, and more robust tracking performance is achieved by eliminating the distractors from the input instance search image. Besides, we use the generalized intersection over union (GIoU) as our training loss. GIoU is a more strict metric for the bounding box regression compared to the traditional IoU, which can be used as training loss for more accurate tracking results. Experiments on five challenging benchmarks have shown favorable and state-of-the-art results against other trackers in different aspects.