Person re-identification by attribute-assisted clothes appearance

Abstract

Person re-identification across nonoverlapping camera views is a challenging computer vision task. Due to the often low video quality and high camera position, it is difficult to get clear human faces. Therefore, clothes appearance is the main cue to re-identify a person. It is difficult to represent clothes appearance using low-level features due to its nonrigidity, but daily clothes have many characteristics in common. Based on this observation, we study person re-identification by embedding middle-level clothes attributes into the classifier via a latent support vector machine framework. We also collect a large-scale person re-identification dataset, and the effectiveness of the proposed method is demonstrated on this dataset under open-set experimental settings.