SiamATA: an asymmetric target-aware and frequency domain task-aware Siamese network for visual tracking

In recent years, Siamese-based trackers have achieved promising results in visual object tracking. However, the feature extraction capability of current popular Siamese-like networks is limited, making it difficult to fully distinguish the object from the background. Trackers are susceptible to drifting caused by factors such as occlusion, scale variation, and fast motion. In this paper, we propose a novel tracker, dubbed Siamese network with asymmetric target-aware and task-aware (SiamATA). The network is based on the asymmetric structure of the classification-regression branches, including the template classification branch, template regression branch, search region classification branch, and search region regression branch, to alleviate overfitting. Meanwhile, a target-aware attention module is introduced to learn powerful context information through spatial attention and selectively emphasize dependency channel features through channel attention, providing target-aware semantic features for each branch. In addition, we adopt the nonlocal pixel-wise correlation method to suppress the influence of similar object interference. Finally, we design a frequency domain task-aware attention module to explore the self-semantic information of classification and regression branches. Extensive experiments demonstrate the effectiveness of our tracker on six benchmarks: OTB100, UAV123, VOT2018, VOT2019, GOT-10K, and LaSOT.