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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.<\/jats:p>","DOI":"10.4018\/ijertcs.2019040104","type":"journal-article","created":{"date-parts":[[2019,3,20]],"date-time":"2019-03-20T13:29:02Z","timestamp":1553088542000},"page":"53-65","source":"Crossref","is-referenced-by-count":1,"title":["A Training Method of Convolution Neural Network for Illumination Robust Pedestrian Detection"],"prefix":"10.4018","volume":"10","author":[{"given":"Junmo","family":"Jeong","sequence":"first","affiliation":[{"name":"Dept. of electronics Engineering, Seokyeong University, Seoul, Korea"}]}],"member":"2432","reference":[{"key":"IJERTCS.2019040104-0","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"IJERTCS.2019040104-1","first-page":"111","article-title":"A theoretical analysis of feature pooling in visual recognition.","author":"Y. 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