Learning meta-adversarial features via multi-stage adaptation network for robust visual object tracking

Visual tracking is a crucial research topic in computer vision, which aims to locate any object as precisely as possible over a sequence of image frames. However, the existing trackers often suffer from the object drifting problem due to the difficulty of adapting to complex environments. In this paper, we propose a novel multi-stage adaptation network (MAN), including the meta-adaptation, feature adaptation, and location adaptation sub-networks, to improve the adaptability and robustness of tracking. Specifically, the meta-adaptation sub-network takes advantage of meta-learning to enhance the generalization ability for the new tracking sequence. The feature adaptation sub-network exploits an adversarial attention mask module and a multi-level and multi-scale meta-classifier module for improving the robustness and discriminative ability. Moreover, the location adaptation sub-network can refine the tracking location to avoid the drifting problem. The three sub-networks can benefit from each other and are strategically integrated in a whole framework. Extensive experimental results demonstrate that the proposed tracker outperforms the state-of-the-art methods on several challenging datasets, including OTB50, OTB2013, OTB100, UAV123, UAV20L, NfS, LaSOT, VOT2016, and VOT2018.