Efficient Person Re-identification by Hybrid Spatiogram and Covariance Descriptor

Feature and metric researchings are two vital aspects in person re-identification. Metric learning seems to have gained extra advantage over feature in recent evaluations. In this paper, we explore the neglected potential of feature designing for re-identification. We propose a novel and efficient person descriptor, which is motivated by traditional spatiogram and covariance descriptors. The spatiogram feature accumulates multiple spatial histograms of different image regions from several color channels and then extracts three descriptive sub-features. The covariance feature exploits several colorspaces and intensity gradients as pixel features and then extracts multiple statistical feature vectors from a pyramid of covariance matrices. Moreover, we also propose an effective and efficient multi-shot re-id metric without learning, which fuses the residual and coding coefficients after collaboratively coding samples on all person classes. The proposed descriptor and metric are evaluated with current methods on benchmark datasets. Our methods not only achieve state-of-the-art results but also are straightforward and computationally efficient, facilitating real-time surveillance applications such as pedestrian tracking and robotic perception in various dynamic scenes.