Learning adaptive updating siamese network for visual tracking

Recently, Siamese network (Siam)-based visual tracking describes the tracking problems as the cross-correlation between convolutional features of the target template and searching regions and solves them by similarity learning, which has achieved great success in performance. However, most of the existing Siam-based tracking methods neglect to explore the feature correlations, which is very important to learn more representative features. Moreover, the first frame is used as the fixed template without updating the template, which leads to a reduction in accuracy. To address these issues, in this paper, we propose an Adaptive Updating Siamese Network (AU-Siam) for more powerful feature correlations and adaptive template updating. Specifically, a siamese feature extraction subnetwork is proposed to introduce the attention mechanism for more discriminative representations. Furthermore, an object template updating subnetwork is developed to dynamically learn object appearance changes for robust tracking. It’s interesting to show that the proposed AU-Siam can effectively reduce the probability of tracking drift in the case of fast motions and heavy occlusion and improve the tracking accuracy. Experimental results on public tracking benchmarks with challenging sequences demonstrate that our AU-Siam performs favorably against other state-of-the-art methods.