Learning multi-region features for vehicle re-identification with context-based ranking method

Abstract

Vehicle re-identification is to identify a target vehicle in different cameras with non-overlapping views. It is a challenging task due to various viewpoints of vehicles, diversified illuminations and complicated environments. Most existing vehicle re-identification methods focus on learning global features, while neglecting the importance of local features. In this paper, we propose a Multi-Region Model (MRM) to learn powerful features for vehicle re-identification. In addition to extracting global region features, MRM also extracts features from a series of local regions. For each local region, instead of utilizing the rigid part to extract features directly, a Spatial Transformer Network (STN) based localization model is introduced to localize local regions which contain more distinctive visual cues. In order to further improve the performance of re-identification, we design a context-based ranking method which generates the ranking list by taking context and content into consideration to measure the similarity between neighbors. Experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset.