Multi-modal metric learning for vehicle re-identification in traffic surveillance environment
Yi Tang, Di Wu, Zhi Juan Jin, Wenbin Zou, Xia Li · 2017
Vehicle re-identification (Re-Id) aims to retrieve the same vehicle captured by disjoint cameras at different time instants from different locations, and is a challenging task mainly due to the high similarity among the captured vehicle images in surveillance environment. With the rapid development of Convolutional Neural Network (CNN), learning-based deep features have been adopted to combine with hand-crafted features to re-identify vehicles in traffic surveillance environment. However, the two kinds of features are in different feature space, and if they are fused directly together, their complementary correlation is not able to be fully explored. To address such an issue, this paper proposes a multi-modal metric learning architecture to fuse deep features and hand-crafted ones in an end-to-end optimization network, which achieves a more robust and discriminative feature representation for vehicle re-identification. The extensive experiments on a large-scale traffic surveillance vehicle dataset demonstrate that our proposed approach substantially outperforms the state-of-the-art methods on vehicle Re-Id.