VSLN: View‐aware sphere learning network for cross‐view vehicle re‐identification

Abstract

Cross-view vehicle Reidentification (ReID) has attracted widespread attention as an increasingly important vision task in intelligent transportation and urban surveillance. Benefiting from Convolutional Neural Network (CNN), recent studies have promoted the development of vehicle ReID by extracting discriminative local features. However, two fundamental challenges of small interclass discrepancy caused by different views and large intraclass distance caused by similar appearance still hinder the performance of cross-view vehicle ReID. In this paper, a novel View-aware Sphere Learning Network (VSLN) is proposed to alleviate the above issues while maintaining the merits of CNN-based approaches to generate view-aware sphere-based features. First, a Sphere Feature Embedding Network (SFEN) is proposed to constrain the images into hypersphere for extracting sphere features. On the other hand, this study presents a sphere similarity triple loss to help SFEN concentrate more on robust and discriminative vehicle parts. Second, since the vehicle images are usually captured from different viewpoints, this study further extends SFEN by introducing a Vehicle Viewpoint Predictor (VVP) combined with global attention mechanism to enlarge the discrepancy of interclass and shorten the distance of intraclass. Moreover, a city-scale data set, named Vehicle from Different Viewpoints, containing image-level viewpoint labels, is collected for training VVP. As a result, the proposed VLSN can achieve 96.31% Top-1 accuracy and 79.46% Top-1 accuracy on VeRi-776 and VRIC data sets, respectively. Overall, extensive experimental results on two benchmark data sets show that the proposed VSLN outperforms state-of-the-art methods.