Learning Part-Based Features for Vehicle Re-Identification with Global Context

Abstract

Re-identification in automated surveillance systems is a challenging deep learning problem. Learning part-based features augmented with one or more global features is an efficient approach for enhancing the performance of re-identification networks. However, the latter may increase the number of trainable parameters, leading to unacceptable complexity. We propose a novel part-based model that unifies a global component by taking the distances of the parts from the global feature vector and using them as loss weights during the training of the individual parts, without increasing complexity. We conduct extensive experiments on two large-scale standard vehicle re-identification datasets to test, validate, and perform a comparative performance analysis of the proposed approach, which we named the global–local similarity-induced part-based network (GLSIPNet). The results show that our method outperforms the baseline by 2.5% (mAP) in the case of the VeRi dataset and by 2.4%, 3.3%, and 2.8% (mAP) for small, medium, and large variants of the VehicleId dataset, respectively. It also performs on par with state-of-the-art methods in the literature used for comparison.