Local Heterogeneous Features for Person Re-Identification in Harsh Environments

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.