Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking
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. RESEARCH PAPER .
November 2020, Vol. 63 212102:1–212102:12 https://doi.org/10.1007/s11432-019-2811-8
Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking Qi WANG1 , Weidong MIN2,3* , Daojing HE4 , Song ZOU1 , Tiemei HUANG1 , Yu ZHANG1 & Ruikang LIU1 1
School of Information Engineering, Nanchang University, Nanchang 330031, China; 2 School of Software, Nanchang University, Nanchang 330047, China; 3 Jiangxi Key Laboratory of Smart City, Nanchang University, Nanchang 330047, China; 4 School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China Received 26 August 2019/Revised 15 November 2019/Accepted 31 January 2020/Published online 13 October 2020
Abstract 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. Keywords
vehicle re-identification, DFN, two-stage re-ranking, fine-grained, Jaccard metric
Citation Wang Q, Min W D, He D J, et al. Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking. Sci China Inf Sci, 2020, 63(11): 212102, https://doi.org/10.1007/s11432-019-2811-8
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Introduction
Re-identification is aimed to identify the same target within the different shooting scenes and periods, which is an important field of computer vision, and vehicle re-identification is one of major topics. A straightforward application is to distinguish whether a vehicle corresponds to the same car model by identifying the license plate [1–3]. Vehicle re-identification can be executed successfully if license plate characters can be accurately registered. However, the analysis of surveillance videos is still associated with the issues owing to license plate recognition loss, various v
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