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Benefiting from its ability to efficiently learn how an object is changing,correlation filters have recently demonstrated excellent performance forrapidly tracking objects. Designing effective features and handling modeldrifts are two important aspects for online visual tracking. This paper tacklesthese challenges by proposing a robust correlation tracking algorithm (RCT)based on two ideas: First, we propose a method to fuse features in order tomore naturally describe the gradient and color information of the trackedobject, and introduce the fused features into a background aware correlationfilter to obtain the response map. Second, we present a novel strategy tosignificantly reduce noise in the response map and therefore ease the problemof model drift. Systematic comparative evaluations performed over multipletracking benchmarks demonstrate the efficacy of the proposed approach.

Robust Correlation Tracking via Multi-channel Fused Features and Reliable Response Map