Vehicle Shape and Color Classification Using Convolutional Neural Network

TL;DR

This paper develops a vehicle reidentification module using deep neural networks for make/model and color classification, demonstrating high accuracy on controlled and video datasets for surveillance applications.

Contribution

It introduces a large-scale labeled dataset and applies deep neural networks to improve vehicle classification accuracy for reidentification tasks.

Findings

Deep neural networks achieved high classification accuracy.

Effective vehicle reidentification demonstrated on video data.

Large-scale dataset facilitated training and evaluation.

Abstract

This paper presents a module of vehicle reidentification based on make/model and color classification. It could be used by the Automated Vehicular Surveillance (AVS) or by the fast analysis of video data. Many of problems, that are related to this topic, had to be addressed. In order to facilitate and accelerate the progress in this subject, we will present our way to collect and to label a large scale data set. We used deeper neural networks in our training. They showed a good classification accuracy. We show the results of make/model and color classification on controlled and video data set. We demonstrate with the help of a developed application the re-identification of vehicles on video images based on make/model and color classification. This work was partially funded under the grant.