A Training Method of Convolution Neural Network for Illumination Robust Pedestrian Detection

Abstract

In this article, the authors propose a new training method of convolution neural networks for pedestrian detection under the illumination of robust environments of ADAS (Advanced Driver Assistance System). This training method was aimed at proposing a method to increase the recognition rate in a system that classifies objects by receiving distorted images in real time as the ADAS, by using the Convolution Neural Network (CNN). The proposed method used images with an increased distortion level by setting gamma to 0.7, and the conventional method was experimented with images with a gamma set to 1. In this article, the authors experiment with the comparison of the conventional training method using the preprocessing accelerator and the proposed training method using the gamma variation. In this study, pedestrian images with a distorted illumination intensity were used in training and then the accuracy of pedestrian classification was tested with normal images and distorted images as test images. The proposed method shows an error rate of 9.8%, which was improved by 1.2% in accuracy.