Adaptive margin nearest Neighbor for person re-identification

Abstract

Person re-identification is a challenging issue due to large visual appearance changes caused by variations in viewpoint, lighting, background clutter and occlusion among different cameras. Recently, Mahalanobis metric learning methods, which aim to find a global, linear transformation of the feature space between cameras [1–4], are widely used in person re-identification. In order to maximize the inter-class variation, general Mahalanobis metric learning methods usually push impostors (i.e., all negative samples that are nearer than the target neighbors) to a fixed threshold distance away, treating all these impostors equally without considering their diversity. However, for person re-identification, the discrepancies among impostors are useful for refining the ranking list. Motivated by this observation, we propose an Adaptive Margin Nearest Neighbor (AMNN) method for person re-identification. AMNN aims to take unequal treatment to each samples impostors by pushing them to adaptive variable margins away. Extensive comparative experiments conducted on two standard datasets have confirmed the superiority of the proposed method.