Camera compensation using feature projection matrix for person re-identification

Matching individuals across a group of spatially non-overlapping surveillance cameras, also known as person re-identification, has recently attracted a lot of research interests. Current methods mainly focus on feature extraction or metric learning, which directly compare person images captured by different cameras, but seldom consider device differences caused by various surveillance conditions, e.g. view switching, scale zooming and illumination variation. Although brightness transfer function was proposed to address the problem of illumination variation, it could not handle view and scale changes among various cameras. In this paper, we propose an effective data-driven method to conquer device differences in the practical surveillance camera network. More precisely, with the help of a set of labelled pair-wise person images captured by two disjoint cameras, a feature projection matrix can be learned to project the person images of one camera to the feature space of the other camera, and thus images from these two different cameras can be accurately compared in a common feature space. Extensive comparative experiments conducted on three standard datasets have shown the promising prospect of our proposed methods.