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Siamese recurrent architecture for visual tracking

Treating visual tracking as a matching problem, siamese architecture has drawn increasing interest recently. In this paper, we propose a novel siamese recurrent architecture that can enhance the similarity matching by leveraging contextual information. Specifically, the multi-directional Recurrent Neural Network (RNN) is employed to memorize the long-range contextual dependencies of object parts and learn the self-structure information of the object. We test the proposed method on a challenging benchmark, and it gain promising results compared with the existing tracking algorithms.