论文标题
通过多渠道融合功能和可靠的响应映射进行强大的相关跟踪
Robust Correlation Tracking via Multi-channel Fused Features and Reliable Response Map
论文作者
论文摘要
受益于其有效学习对象如何变化的能力，相关性过滤器最近在快速跟踪对象方面表现出了出色的性能。设计有效的功能和处理模型漂移是在线视觉跟踪的两个重要方面。本文通过提出基于两个想法的强大相关跟踪算法（RCT）来应对这些挑战：首先，我们提出了一种融合功能的方法，以便更自然地描述跟踪对象的梯度和颜色信息，并将融合功能引入背景感知相关滤波器以获取响应映射。其次，我们提出了一种新的策略，可以显着减少响应图中的噪声，从而缓解模型漂移的问题。对多个跟踪基准进行的系统比较评估证明了该方法的功效。
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.
