Object tracking is a key component of machine vision system and getting much attention in different walk of life. Recently, correlation filters have been successfully applied to visual tracking. However, how to design effective features and deal with model drifts remain open issues for online tracking. This paper tackles these challenges by proposing a real-time correlation tracking algorithm (RCT) based on two ideas. First, we propose a method to fuse features to more naturally describe the gradient and color information of the tracked object, and introduce the fused feature 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. The results show that the proposed RCT significantly improves the performance compared to the baseline tracker while still maintaining a real-time tracking speed of 26 fps in MATLAB implementation.

目标跟踪是机器视觉系统的关键组成部分，在各行各业都备受关注。最近，相关滤波器已成功应用于视觉跟踪。然而，如何设计有效的特征和处理模型漂移仍然是在线跟踪的悬而未决的问题。本文通过提出一种基于两种思想的实时相关跟踪算法（RCT）来应对这些挑战。首先，我们提出了一种融合特征的方法，以更自然地描述被跟踪对象的梯度和颜色信息，并将融合特征引入背景感知相关滤波器以获得响应图。其次，我们提出了一种新的策略来显着降低响应图中的噪声，从而缓解模型漂移问题。在多个跟踪基准上进行的系统比较评估证明了所提出方法的有效性。结果表明，与基线跟踪器相比，所提出的 RCT 显着提高了性能，同时在 MATLAB 实现中仍保持 26 fps 的实时跟踪速度。