Detail enhancement-based vehicle re-identification with orientation-guided re-ranking

Abstract

Vehicle re-identification (re-ID) has attracted extensive attention in the field of computer vision owing to the development of smart cities. Two issues remain in the task of vehicle re-ID: minor inter-class differences and extreme orientation variations. To address them, we propose a detailed enhancement-based vehicle re-ID method with orientation-guided re-ranking. In the proposed method, a novel enhancement module using stripe-based partition, is presented to avoid the negative influence of minor inter-class difference. The stripe-based partition subdivides the feature map of middle layers in the network into two stripes, and then the module explores more detailed information for vehicle re-ID. Furthermore, a multilayer feature fusion module is proposed to enhance the feature representation of a vehicle. To address the problem of extreme orientation variation in vehicle re-ID, we propose an orientation-guided re-ranking strategy to re-rank the retrieval result based on the orientation information and query expansion algorithm. This strategy can optimize the ranking of vehicles whose orientation is not similar to the query images in the post-processing stage. Extensive experiments on three public datasets confirm the effectiveness of our method.