Moving towards in object recognition with deep learning for autonomous driving applications

Abstract

Object recognition and pedestrian detection are of crucial importance to autonomous driving applications. Deep learning based methods have exhibited very large improvements in accuracy and fast decision in real time applications thanks to CUDA support. In this paper, we propose two Convolutions Neural Networks (CNNs) architectures with different layers. We extract the features obtained from the proposed CNN, CNN in AlexNet architecture, and Bag of visual Words (BOW) approach by using SURF, HOG and k-means. We use linear SVM classifiers for training the features. In the experiments, we carried out object recognition and pedestrian detection tasks using the benchmark the Caltech 101 and the Caltech Pedestrian Detection datasets.