SiamON: Siamese Occlusion-Aware Network for Visual Tracking

Abstract

Occlusion has been proven to be one of the most challenging factors faced by most visual trackers. There are mainly two difficulties, the first one is that the number of occlusion samples are very limited even though collecting a large-scale training data set, and another one is how to correctly learn the features of the target when comes to occlusion situations. In this paper, we tried to solve these two problems together in our proposed model. To this end, we propose a novel Siamese Occlusion-aware Network (SiamON) for high-performance visual tracking. In particular, we predefine some soft-masks to solve the problem of fewer occlusion samples, which perceive patterns of occlusion contents at different locations and take these masks as the conditions to guide occlusion-aware feature learning. Meanwhile, we propose a target-aware attention mechanism allows the model to pay more attention to the target and further weaken the impact of occlusion. Extensive experiments on several popular benchmarks show that our tracking method exceeds many state-of-the-art trackers especially in the presence of occlusion and meets the requirements of real-time.