MRNet: A Keypoint Guided Multi-scale Reasoning Network for Vehicle Re-identification

Abstract

AbstractWith the increasing usage of massive surveillance data, vehicle re-identification (re-ID) has become a hot topic in the computer vision community. Vehicle re-ID is a challenging problem due to the viewpoint variation, i.e. the different views greatly affect the visual appearance of a vehicle. To handle this problem, we propose an end-to-end framework called Keypoint Guided Multi-Scale Reasoning Network (MRNet) to infer multi-view vehicle features from a one-view image. In our proposed framework, besides the global branch, we learn multi-view vehicle information by introducing a local branch, which leverages different vehicle segments to do relational reasoning. MRNet can infer the latent whole vehicle feature by increasing the semantic similarity between incomplete vehicle segments. MRNet is evaluated on two benchmarks (VeRi-776 and VehicleID) and the experimental results show that our framework has achieved competitive performance with the state-of-the-art methods. On the more challenging dataset VeRi-776, we achieve 72.0% in mAP and 92.4% in Rank-1. Our code is available at https://github.com/panmt/MRNet_for_vehicle_reID.KeywordsVehicle re-identificationRelational reasoningMulti-view features