LAUSR

Vehicle re-identification, which aims to retrieve information regarding a vehicle from different cameras with non-overlapping views, has recently attracted extensive attention in the field of computer vision owing to the development of smart cities. This task can be regarded as a type of retrieval problem, where re-ranking is important for performance enhancement. In the vehicle re-identification ranking list, images whose orientations are dissimilar to that of the query image must preferably be optimized on priority. However, traditional methods are incompatible with such samples, resulting in unsatisfactory vehicle re-identification performances. Therefore, in this study, we propose a vehicle re-identification re-ranking method with orientation-guide query expansion to optimize the initial ranking list obtained by a re-identification model. In the proposed method, we first find the nearest neighbor image whose orientation is dissimilar to the queried image and then fuse the features of the query and neighbor images to obtain new features for information retrieval. Experiments are performed on two public data sets, VeRi-776 and VehicleID, and the effectiveness of the proposed method is confirmed.