Person Re-Identification Based on Spatiogram Descriptor and Collaborative Representation

Abstract

Feature and metric designing are two vital aspects in person re-identification. In this letter, we firstly propose a novel spatiogram based person descriptor. Such spatiograms of different image regions from several color channels are calculated and accumulated to create a histogram vector and two distinctive spatial statistical vectors. Secondly, through further investigating the multi-shot set-based metric based on the recent collaborative representation model, we propose an effective and efficient multi-shot metric, which fuses the residual and coding coefficients after collaboratively coding samples on all person classes. Finally, we evaluate the proposed descriptor and metric with other published methods on benchmark datasets. Our methods not only achieve state-of-the-art results but also are novel, straightforward and computationally efficient, which will facilitate the real-time surveillance applications such as pedestrian tracking.