Methods of the Vehicle Re-identification

TL;DR

This paper presents two vehicle re-identification methods: one based on classification requiring a search image, and another using shape and color features for cases without a search image, demonstrating their effectiveness on datasets and video images.

Contribution

It introduces a dual approach to vehicle re-identification, including a fine-grained classification of make, model, year, and perspective, and a shape-color feature method for broader applicability.

Findings

The classification method improves re-identification accuracy on VRIC and VehicleID datasets.

The shape and color feature method effectively re-identifies vehicles without a search image.

Demonstrated successful vehicle re-identification on video and controlled datasets.

Abstract

Most of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. VRIC and VehicleID data set are suitable for training this module. It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification. The second method takes as input a representative image of the search vehicle with similar make/model, released year and colour. It is very useful when an image from the search vehicle is not available. It produces as output a shape and a colour features. This could be used by the matching across a database to re-identify vehicles, which look similar to the search vehicle. To get a robust module for the re-identification, a fine-grained classification has been trained, which its class consists of four elements: the make of a vehicle refers to the vehicle's manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to type of model within that manufacturer's portfolio, e.g. C Class, the year refers to the iteration of the model, which may receive progressive alterations and upgrades by its manufacturer and the perspective of the vehicle. Thus, all four elements describe the vehicle at increasing degree of specificity. The aim of the vehicle shape classification is to classify the combination of these four elements. The colour classification has been separately trained. The results of vehicle re-identification will be shown. Using a developed tool, the re-identification of vehicles on video images and on controlled data set will be demonstrated. This work was partially funded under the grant.