A person re-identification algorithm by using region-based feature selection and feature fusion

In outdoor surveillance, person appearances captured by different cameras have obvious variations due to different poses and viewpoints, which affect the accuracy of person re-identification. In this paper, a person re-identification algorithm by using region-based feature selection and future fusion is proposed to divide one body into the upper region and the lower region. According to their different characteristics, each region adopts different kinds of features, which can efficiently reduce the negative impact from different poses and viewpoints. Moreover, since different features of one region may have different intrinsic meanings, during the feature fusion, different features of one region are separately represented instead of being comprehensively processed. The proposed feature fusion can make full use of the salience of different features. The experimental results demonstrate that the proposed algorithm improves the accuracy of person re-identification compared with the state of the art.