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The Scientific World Journal
Volume 2014, Article ID 196415, 8 pages
Research Article

Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle

Graduate School of Electrical Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea

Received 9 April 2014; Revised 7 June 2014; Accepted 8 June 2014; Published 10 July 2014

Academic Editor: Yu-Bo Yuan

Copyright © 2014 Joko Hariyono et al. 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.


This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.