# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 1 Introduction  
CCTV cameras are an effective and pervasive component of most modern surveillance systems that have numerous applications, including law enforcement, event monitoring, process monitoring, crime analysis, and other smart city processes [1]. They generate vast amounts of image and video data for these systems. The tasks performed by surveillance systems that deal with such data can be narrowed down to activities like recognition, identification, re-identification, tracking, or the retrieval of objects, persons, or vehicles [2]. Various tasks, like object detection [3] and classification [4], can be performed on the data to generate important insights. In this paper, we focus specifically on vehicle re-identification (VReID), which involves retrieving an image of a particular vehicle from a database of vehicle images, given a particular vehicle image as a query. In surveillance applications, VReID is used in traffic- and fleet-management-based industrial processes. The VReID problem is closely related to person re-identification, which has been more thoroughly studied and researched than VReID. Successful results in person re-identification, usually achieved through deep learning, have inspired parallel research on VReID, and in some aspects, the latter often poses more challenges to the problem of re-identification matching. In any re-identification problem, discriminating features are extracted from the 'query' image to find matches from a collection of 'gallery' images. Methods used in person re-identification have been found to work well in the case of vehicle re-identification. Initially, some handcrafted image-processing-based features were used along with deep learning features [5, 6]. However, due to the low performance of these features, subsequent works have utilized deep learning-based methods only for feature extraction. Most methods use backbone models pre-trained on ImageNet [7], like AlexNet [5, 7], VGG [8, 9], GoogLeNet [6, 10], ResNet [11, 12], and MobileNet [13], for feature extraction. The features can be attribute-based, local component-based, part-based, or global deep features. Attribute-based features include global features like vehicle color, make, model, and viewpoint. The attributes are first learned using a separate dedicated module/network, and then the learned attributes are mapped to labels. This causes an inherent disadvantage in such methods with respect to increased complexity, as a separate module is required for attribute identification. Local features can be one of two types: (i) salient features directly extracted from vehicle images like headlights, mirrors, number plates, back lights, etc., or (ii) features extracted from a deeply learned feature vector. The latter are termed part-based local features. In this approach, the convolutional tensor obtained after the backbone model is partitioned into different parts to obtain local representations [14]. Attention layers have also been employed in some works to generate localized discriminative features [15, 16]. The global deep features are generally features generated by the backbone model. Conventionally, approaches used in VReID use a combination of part-based layers along with a global layer. The layers obtained from the deeply learned global features proceed in parallel in different branches, which are all trained by fully connected layers. These local features can be used independently or together with a global network to generate a feature map. We propose a different approach in which we unify global and part-based features without actually training any global branches. In this paper, we set out to increase the accumulated body of knowledge on part-based re-identification methods by proposing a novel approach to unifying global and part-based features. In this method, we initially perform a generalized uniform partition on the convolutional tensor generated after passing the image through the backbone network.