No 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.INDEX TERMS Person re-identification, camera networks, feature integration, convolutional neural network.

No abstract

No abstract

Local features could learn semantic information for pedestrian images and they are very important for person re-identification (Re-ID) in harsh environments. However, most approaches only optimize one kind of local feature, which results in incomplete local features. In this paper, we propose Local Heterogeneous Features (LHF) to extract discriminative local features from three aspects. To this end, we utilize three kinds of losses to learn three kinds of local features, i.e., local discriminative features, local relative features, local compact features. As for local discriminative features, we split the attention maps into three horizontal sub-regions and perform the classification operation. Then, we divide the attention maps into two horizontal sub-regions, and we synchronously apply the triplet loss and center loss to learn local relative features and local compact features. Finally, we utilize local discriminative features to represent pedestrian. We evaluate LHF on public person Re-ID datasets and prove LHF is meaningful for local feature learning.INDEX TERMS Person re-identification, local heterogeneous features, harsh environments.