Evaluation of color spaces for person re-identification

Person re-identification is an important problem in visual surveillance where appearance plays a key role. Color is one of the widely used appearance features and utilizing more color spaces doesn't imply benefit of performance enhancement. That's because the poor performance color spaces influence on the high ones. So it is significant to evaluate the performance of different color spaces for person re-identification. In this paper, we propose a novel approach, called as random ensemble of color features (RECF), where we build a random forest to learn the similarity function of pairs of person images using color features from 6 kinds of popular color spaces (RGB, normalized RGB (NRGB), HSV, YCbCr, CIE XYZ and CIE Lab). We carry out experiments on the challenging dataset VIPeR to show the performances of different color spaces and their combination. We find out that the combination of NRGB, HSV, YCbCr and CIE Lab color spaces achieves the best performance, and our approach alleviates the over-fitting problem when there are limited training data.