Abstract: To reduce the influence of factors such as illumination variation, scale variation, partial occlusion in target tracking, a target tracking algorithm based on Kernelized Correlation Filter (KCF) with block-based model was proposed. Firstly, the feature of histogram of oriented gradients and the feature of color name were combined to better characterize the target. Secondly, the method of scale pyramid was adopted to estimate the target scale. Finally, the peak to sidelobe ratio of the feature response map was used to detect occlusion, and the partial occlusion problem was solved by introducing a high-confidence block relocation module and a dynamic strategy for model adaptive updating. To verify the effectiveness of the proposed algorithm, comparative experiments with several mainstream algorithms on various datasets were conducted. Experimental results show that the proposed algorithm has the highest precision and success rate which are respectively 11.89% and 15.24% higher than those of KCF algorithm, indicating that the proposed algorithm has stronger robustness in dealing with factors like illumination variation, scale variation and partial occlusion.

Key words: Kernelized Correlation Filter (KCF), feature fusion, scale estimation, partial occlusion, model updating

摘要： 为了降低目标追踪过程中光照变化、尺度变化、局部遮挡等因素的影响，提出一种引入目标分块模型的核相关滤波（KCF）目标追踪算法。首先，通过融合方向梯度直方图特征和色名属性特征来更好地表征目标；其次，通过构建尺度金字塔对目标进行尺度预测；最后，利用特征响应图的峰值旁瓣比值检测遮挡，并通过引入高置信度分块重定位模块和模型自适应动态更新来处理局部遮挡问题。在多个数据集上与当前多个主流算法进行对比实验，实验结果表明，所提算法具有最高精度和成功率，且比KCF算法分别提升了11.89%和15.24%，表明所提算法在应对光照变化、尺度变化、局部遮挡等因素时具有更强的鲁棒性。

关键词: 核相关滤波, 特征融合, 尺度变化, 局部遮挡, 模型更新