关键词: 目标跟踪, 特征融合, 尺度自适应, 卡尔曼滤波, 平均峰值-相关能量（APCE）, 核相关滤波

Abstract:

In order to solve the problem of occlusion, scale change and background clutter in visual object tracking, based on the kernelized correlation filter algorithm, the average peak correlation energy occlusion criterion is introduced, an Adaptive fusion Multi-Feature anti-occlusion Kernel Correlation Filter algorithm is proposed （AMFKCF）. Firstly, it initializes target features and scale factors. Then, the position and scale filters are trained by extracting and fusing multiple features and scale factors, the center position response of the target is obtained. Finally, according to the occlusion criteria, Kalman filter is introduced to compensate the uncovered and occluded location of the objec center. The AMFKCF and the mainstream tracking algorithm are challenged in CVPR 2013 benchmark. Experimental results show that compared with the mainstream algorithm, the accuracy of the proposed algorithm is improved by 0.115, the success rate is increased by 0.083, the center position error is increased by 14.67 pixels, and the distance accuracy is improved by 9.75 percentage points.

Key words: target tracking, feature fusion, scale adaptation, Kalman filter, Average Peak-to Correlation Energy（APCE）, kernelized correlation filter