基于多核融合的目标大面积遮挡处理方法

提出了一种基于多核融合的目标遮挡处理方法,用于提高大面积遮挡情况下视觉目标跟踪算法的鲁棒性和准确性.与现有基于单个对称核加权直方图的mean shift跟踪算法不同,该方法以目标区域内的多个非中心位置为核函数中心,构建多个非对称核加权直方图.由于这些直方图对目标的不同区域赋予了不同的权重,使得在遮挡发生时总存在一些直方图受影响较小.依据各个直方图分别进行mean shift迭代获得一组目标位置估计后,利用D-S证据理论融合判定最终的目标位置.实验结果表明,该方法在目标被大面积遮挡时仍能够获得准确的跟踪.

Occlusion handling approach in visual tracking based on multiple-kernel fusion

A novel visual tracking approach based on multiple-kernel fusion was proposed to improve robustness and accuracy of tracking under large-area occlusion.Unlike traditional single symmetric kernel weighted histogram used in mean shift tracking,this approach adopted several asymmetric kernel functions centered at different positions within target region to build a set of asymmetric kernel weighted histograms.Because these histograms weighted each part of the target region differently,there must be some less influenced histograms during occlusion.Based on each histogram,a set of target location estimations were provided respectively by mean shift iteration,and the target location was obtained by fusing these estimations using Dempster-Shafer evidence theory.The experimental results demonstrate the effectiveness of the proposed approach under large-area occlusion.