A Cooperative Tracker by Fusing Correlation Filter and Siamese Network

Abstract

The robustness of model-free trackers is always supported by a model updater and a motion model. However, most state-of-the-art trackers (e.g. correlation-filter or Siamese-network based trackers) are unbalanced in both aspects. Consequently, they drift easily when encountering challenging scenarios such as fast motion, occlusion or background clutter. Inspired by the complementarity of different tracking mechanisms, we propose an adaptive cooperation tracker, where correlation filter and Siamese networks complement each other in their shortcomings. Specifically, our tracker consists of three components: a context-aware correlation filter network (termed as CaCFNet), a Siamese network and a tracking failure estimator. In the online tracking, the Siamese network component locates the target coarsely in a larger search region, and then CaCFNet refines the coarse position for higher accuracy. The Siamese network component is activated adaptively according to the result of failure estimator, which keeps the tracker in real time and avoids interference between two different mechanisms. Moreover, context-aware correlation filter network and Siamese network are trained offline for better feature representation for visual tracking task. Comprehensive experiments are performed on three popular benchmark: OTB2013, OTB2015, VOT2017 to demonstrate the effectiveness of the proposed tracker, and the proposed tracker achieves state-of-the-art results on these benchmark.