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Abstract. We proposed a structure of processing Convolution Neural Network(CNN) algorithm which applied Filter Buffers. Devices of smaller sizes and lower power consumptions are used in smart mobile devices or hardware used in an Intelligent Advanced Driver Assistance Systems. In these cases, it is needed to access external memory for a limited number of Process Elements. We propose the method of increasing the performance of processing by eliminating the unnecessary accesses of external memory. This is a useful algorithm for a parallel processing of an artificial neural network. It has been shown that the ratio of external memory accesses has decreased by 20%, and the processing performance has increased.

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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.

This paper proposed a pedestrian recognition using CENTRIST