Context-aware Siamese network for object tracking

At present, temporal and spatial contexts are widely used to improve the adaptability of a tracker. However, most existing methods usually focus on one aspect of the temporal or spatial context and rarely exploit them simultaneously. In this paper, a context-aware Siamese Network (CSNet) is proposed, which skilfully integrates the modelling of temporal and spatial context into the Siamese tracking framework. Specifically, CSNet consists of a context-based channel attention module and a context-based cross-attention module. The former aggregates spatial context information from different channels and dynamically emphasizes target features, which makes it easier for the tracker to distinguish the target from the background. The latter propagates the temporal context from the previous frames to the current frame to establish the part-level relationship between the search region and the historical target state, which enables the tracker better adapt to the target deformation. In addition, to further mine context information, the CSNet is equipped with a state-aware strategy to control the contribution of different context information in tracking. Extensive experiments on OTB2015, UAV123, GOT-10k, LaSOT, and TrackingNet show that the proposed tracking method achieves comparable performance to the advanced trackers.