Data Augmentation and Clustering for Vehicle Make/Model Classification

TL;DR

This paper introduces a vehicle make/model classification method that combines data augmentation, clustering, and a ResNet-based CNN to improve accuracy and robustness in traffic systems, tested on video re-identification tasks.

Contribution

It presents a novel approach integrating data augmentation, clustering, and bias elimination in CNNs for enhanced vehicle classification accuracy.

Findings

Improved classification accuracy with clustering and bias removal.

Enhanced robustness in vehicle re-identification.

Effective application on video data for vehicle re-identification.

Abstract

Vehicle shape information is very important in Intelligent Traffic Systems (ITS). In this paper we present a way to exploit a training data set of vehicles released in different years and captured under different perspectives. Also the efficacy of clustering to enhance the make/model classification is presented. Both steps led to improved classification results and a greater robustness. Deeper convolutional neural network based on ResNet architecture has been designed for the training of the vehicle make/model classification. The unequal class distribution of training data produces an a priori probability. Its elimination, obtained by removing of the bias and through hard normalization of the centroids in the classification layer, improves the classification results. A developed application has been used to test the vehicle re-identification on video data manually based on make/model and color classification. This work was partially funded under the grant.