Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 387-392 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.947

Pedestrian Detection Using HOG Feature-Based Cascade Classifier with Vehicle Black-Box Camera for Supporting Driver Assistance in Urban Road Environments

Jong Bae Kim

Abstract—Abstract—In this paper, we propose a method to detect pedestrians in real time from road images obtained from a black-box, which is a vehicle image recording camera, and to automatically provide pedestrian appearance information to the driver. To detect a pedestrian in the input road image, the proposed method applies a pedestrian detector using a cascade learning device based on the histogram of oriented gradients (HOG) feature information. The pedestrian detector uses the cascade learning device to extract feature information about the pedestrian area based on the histogram description feature information for pedestrian learning. The pedestrian detector detects the candidate pedestrian areas, and the final pedestrian area is detected through the pedestrian verification process. The results of applying the proposed method to urban road images indicate that the accuracy of detection is approximately 93%.

Index Terms—Pedestrian detection, HOG, ADAS, vehicle black-box camera, cascade learning.

J. B. Kim is with the Department of Computer and Software, Sejong Cyber University, Seoul, 04992, S. Korea (e-mail: jb.kim@sjcu.ac.rk).

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Cite: Jong Bae Kim, "Pedestrian Detection Using HOG Feature-Based Cascade Classifier with Vehicle Black-Box Camera for Supporting Driver Assistance in Urban Road Environments," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 387-392, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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