Motorcycle Classification in Urban Scenarios using Convolutional Neural Networks for Feature Extraction

TL;DR

This paper develops a motorcycle classification system for urban environments using CNN feature extraction with AlexNet, achieving over 99% accuracy by combining CNNs with SVM classifiers.

Contribution

It demonstrates the effectiveness of using pre-trained CNN features with SVMs for high-accuracy motorcycle classification in urban scenarios.

Findings

Achieved 99.40% accuracy on three-class classification.

Achieved 99.29% accuracy on five-class classification.

Validated the approach with satisfactory results on a separate dataset.

Abstract

This paper presents a motorcycle classification system for urban scenarios using Convolutional Neural Network (CNN). Significant results on image classification has been achieved using CNNs at the expense of a high computational cost for training with thousands or even millions of examples. Nevertheless, features can be extracted from CNNs already trained. In this work AlexNet, included in the framework CaffeNet, is used to extract features from frames taken on a real urban scenario. The extracted features from the CNN are used to train a support vector machine (SVM) classifier to discriminate motorcycles from other road users. The obtained results show a mean accuracy of 99.40% and 99.29% on a classification task of three and five classes respectively. Further experiments are performed on a validation set of images showing a satisfactory classification.