基于全局背景与特征降维的视觉跟踪算法

Visual Tracking Algorithm Based on Global Context and Feature Dimensionality Reduction

相关滤波算法容易受到形变、运动模糊、相似背景等因素的干扰,导致跟踪任务失败。为了克服以上问题,该文提出一种基于全局背景与特征降维的视觉跟踪算法。该算法首先提取紧邻目标的图像区域作为负样本供分类器学习,以抑制相似背景的干扰;然后提出一种基于主成分分析的更新策略,构建降维矩阵压缩HOG特征的维度,在更新分类器的同时减少其冗余度;最后加入颜色特征表征运动目标,并根据特征对系统状态的响应强度进行自适应融合。在标准数据集上将该文提出的算法与Staple, KCF等其他算法进行了仿真对比,结果表明该文算法具有更强的鲁棒性,在形变因素的影响下,所提出的算法与Staple和KCF算法相比距离精度分别提升8.3%和13.1%。

Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed, constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1% respectively in median distance precision.