Title: Enhancing pedestrian detection using optical flow for surveillance

Authors: Redwan A.K. Noaman; Mohd Alauddin Mohd Ali; Nasharuddin Zainal

Addresses: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, Selangor, Malaysia ' Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, Selangor, Malaysia ' Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, Selangor, Malaysia

Abstract: Optical flow can be used to segment a moving object from its backgrounds and track it. In this paper, an Enhanced Lucas-Kanade optical flow technique was used to improve human detection in terms of speed and accuracy. We combined object segmentation output with a human detector using an optical flow algorithm. The proposed technique used the optical flow to find the area of interest to complete object segmentation and use those results as an input for the human detector. This technique has been developed to be used in surveillance systems. Our experiments indicated that the proposed method was 37% faster and 118% more accurate than the standard Felzenszwalb (PFF) detector.

Keywords: optical flow; Luckas-Kanade; Gaussian filter; human detection; PFF detector; pedestrian detection; surveillance; pedestrians; object detection; object segmentation.

DOI: 10.1504/IJCVR.2017.081246

International Journal of Computational Vision and Robotics, 2017 Vol.7 No.1/2, pp.35 - 48

Received: 04 Dec 2014
Accepted: 26 Feb 2015
Published online: 01 Jan 2017 *

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