Weighted local metric learning for person re-identification

Abstract

Person re-identification aims to match individual across non-overlapping camera networks. In this paper, we propose a weighted local metric learning (WLML) method for person re-identification. Motivated by the fact that local metric learning has been exploited to handle the data which varies locally, we break down the pedestrian images into several local sub-regions, among which different metric functions are learned. Then we use structured method to learn the weight for each metric function and the final distance is calculated from a weighted sum of these metric functions. Our approach can also combine the local metric functions with global metric functions to exploit their complementary strengths. Moreover it is possible to integrate multiple visual features to further promote the recognition rate. Experiments on two challenging datasets validate the effectiveness of our proposed method.