Discriminative Structural Metric Learning for Person Reidentification in Visual Internet of Things

Abstract

Recently, visual Internet of Things (VIoT) has been deployed in many critical missions that are related to society security, such as anti-terrorism, abnormal event detection, crisis monitoring surveillance, etc. In this paper, we focus on a key fundamental problem in VIoT, person reidentification, which aims to correlate people with appropriate labels. The same person captured by various visual sensors in VIoT appears different significantly. To effectively measure the similarity between image pairs, we propose a novel method named discriminative structural metric learning (DSML), which utilizes intraregion metric, weak extra-region metric and extra-region metric to fully mine the structural information of pedestrian in a local way. According to DSML, we obtain a vector where each element is a local similarity score between two subregions. In order to aggregate the local similarity scores into a global one, we further propose a novel aggregating similarity score method named discriminative subregion aggregating (DSA). The DSA could learn the discriminative subregion by assigning different weights for each subregion. The experimental results demonstrate that the proposed method achieves better performance than the state-of-the-art methods.