Improved mean shift algorithm with multi-cue integration and histogram intersection
The mean shift tracker is commonly used in realtime target tracking. However, the original mean shift tracker employs only color feature and uses the Bhattacharya coefficient as similarity measure, resulting in low tracking accuracy. This paper proposed a novel tracking algorithm, which integrated color and texture features and employed histogram intersection and Powells method to track. Firstly, texture feature was extracted by the Local Binary Pattern texture operator and integrated with color feature adaptively. Log-likelihood ratio histogram was proposed to represent objects instead of histogram. Then, the rough location of the target was obtained by the mean shift algorithm based on the two features. Finally, histogram intersection was defined as the similarity metric between the target model and candidates and iteratively maximized by Powells method. Experimental results demonstrate the proposed method can track targets more accurately and fast.
target tracking Mean Shift multi-cue integration histogram intersection Powells method
Mao Dun Xu JiangHu
Electronics Engineering College Naval University of Engineering Wuhan, China
国际会议
武汉
英文
142-146
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）