Discrimination-Aware Integration for Person Re-Identification in Camera Networks

Abstract

In this paper, we propose a novel method named Discrimination-Aware Integration (DAI) for person re-identification (re-ID) in camera networks, which not only integrates multiple re-ID models but also adaptively learns integration weights for different feature dimensions. To avoid the tough selection of deep models, we employ different data sources to train the same re-ID model for learning features from different views, and then, we obtain multiple features for each pedestrian image. To effectively integrate these features, the proposed DAI learns integration weights for each feature dimension according to their importance. Finally, the features extracted from different re-ID models are integrated with the learned integration weights to form the final representation for the pedestrian images. We evaluate the performance of the proposed DAI on three public large-scale person re-ID datasets, i.e., Market1501, CUHK03, and DukeMTMC-reID, and the experimental results show that the proposed DAI outperforms the state-of-the-art results.