Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking

Research on the application of vehicle re-identification to video surveillance has attracted increasingly growing attention. Existing methods are associated with the difficulties of distinguishing different instances of the same car model owing to the incapability of recognizing subtle differences among these instances and the possibility that a subtle difference may lead to incorrect results of ranking. In this paper, a discriminative fine-grained network for vehicle re-identification based on a two-stage re-ranking framework is proposed to address these issues. This discriminative fine-grained network (DFN) is composed of fine-grained and Siamese networks. The proposed hybrid network can extract discriminative features of the vehicle instances with subtle differences. The Siamese network is rather suitable for general object re-identification using two streams of the network, while the fine-grained network is capable of detecting subtle differences. The proposed two-stage re-ranking method allows obtaining a more reliable ranking list by using the Jaccard metric and merging the first and second re-ranking lists, where the latter list is formed using the sample mean feature. Experimental results on the VeRi-776 and VehicleID datasets show that the proposed method achieves the superior performance compared to the state-of-the-art methods used in vehicle re-identification.