  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.
