Robust Correlation Tracking via Multi-channel Fused Features and Reliable Response Map
This paper aims to improve the robustness of online visual tracking for computer vision researchers by tackling feature design and model drift, which are incremental improvements to existing methods.
This paper proposes a robust correlation tracking algorithm (RCT) that addresses challenges in feature design and model drift. It introduces a method to fuse gradient and color features for object description and a strategy to reduce noise in the response map, which helps mitigate model drift.
Benefiting from its ability to efficiently learn how an object is changing, correlation filters have recently demonstrated excellent performance for rapidly tracking objects. Designing effective features and handling model drifts are two important aspects for online visual tracking. This paper tackles these challenges by proposing a robust correlation tracking algorithm (RCT) based on two ideas: First, we propose a method to fuse features in order to more naturally describe the gradient and color information of the tracked object, and introduce the fused features into a background aware correlation filter to obtain the response map. Second, we present a novel strategy to significantly reduce noise in the response map and therefore ease the problem of model drift. Systematic comparative evaluations performed over multiple tracking benchmarks demonstrate the efficacy of the proposed approach.