Visual Cognition Inspired Vehicle Re-identification via Correlative Sparse Ranking with Multi-view Deep Features

Abstract

Vehicle re-identification has gradually gained attention and widespread applications. However, most of the existing methods learn the discriminative features for identities by single feature channel only. It is worth noting that visual cognition of human eyes is a multi-channel system. Therefore, integrating the multi-view information is a nature way to boost computer vision tasks in challenging scenarios. In this paper, we propose to mine multi-view deep features via correlative sparse ranking for vehicle re-identification. Specifically, first, we employ ResNet-50 and GoogleNet as two baseline networks to generate the attributes (vehicle color and type) aggregated features. Then we explore the feature correlation via enforcing the correlation term into the multi-view sparse coding framework. The original rankings are obtained by the reconstruction coefficients between probe and gallery. Finally, we utilize a re-ranking technique to further boost the performance. Experimental results on public benchmark VeRi-776 dataset demonstrate that our approach outperforms state-of-art approaches.