Multi-attribute driven vehicle re-identification with spatial-temporal re-ranking

Vehicle re-identification (re-id) is a promising topic, which focuses on retrieving the same vehicles across different cameras. It is challenging due to the variations of illumination and camera viewpoints. To solve these problems, we present a multi -attribute driven vehicle re-id approach to learn discriminative representations. The proposed approach consists of a multi-branch architecture and a re-ranking strategy. The multi-branch architecture extracts color, model, and appearance features, which explicitly leverages the vehicle attribute cues to enhance the generalization ability, especially for the different vehicles with similar appearance and the same vehicles with different orientations. The re-ranking strategy introduces the spatial-temporal relationship among vehicles from multiple cameras to construct the similar appearance sets and utilizes Jaccard distance between these similar appearance sets to re-rank. Extensive experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art re-id methods on the popular VeRi-776 dataset and VehiclelD dataset.