Siamese Attention Networks with Adaptive Templates for Visual Tracking

Abstract

Visual object tracking takes an important role in realistic applications, such as video understanding, unmanned auto vehicles, and autonomous robots. Although the Siamese-based tracker has achieved good performance in tracking tasks, the existing methods using initial template or updating template with simple strategy result in the performance degradation of the model when the target varies in realistic scenarios such as target occlusion, scale variation, and deformation. In this paper, we propose a visual tracking framework with adaptive template update and spatiotemporal attention, named SiamAttnAT. Specially, we propose a historical template selecting strategy and a template adaptively generating method for robust tracking. In addition, we apply the proposed mechanisms to the employed baseline SiamRPN++. Extensive experiments and comparisons with state-of-the-art trackers on short-term and long-term visual tracking benchmarks including VOT2018, OTB-100, UAV123, NFS, and LaSOT show that the proposed framework achieves the outstanding performance with a considerable real-time speed, verifying its efficiency and effectiveness.