Title: Motion periodicity-based pedestrian detection and particle filter-based pedestrian tracking using stereo vision camera

Authors: Khalid Al-Mutib; Muhammad Emaduddin; Mansour AlSulaiman; Hedjar Ramdane; Ebrahim Mattar

Addresses: Department of Computer Engineering, College of Computer Science and Information, King Saud University, P.O. Box 51178, Kingdom of Saudi Arabia ' Department of Computer Engineering, College of Computer Science and Information, King Saud University, P.O. Box 51178, Kingdom of Saudi Arabia ' Department of Computer Engineering, College of Computer Science and Information, King Saud University, P.O. Box 51178, Kingdom of Saudi Arabia ' Department of Computer Engineering, College of Computer Science and Information, King Saud University, P.O. Box 51178, Kingdom of Saudi Arabia ' Electrical and Electronics Engineering Department, College of Engineering, University of Bahrain, P.O. Box 13184, Kingdom of Bahrain

Abstract: A methodology that detects harmonic motions of limbs and body during a typical human walk is presented. It temporally propagates the position, stride, direction and phase using a particle filter. This is based on a human limb-motion model, and is able to track the walking pedestrians in a heavily occluded environment. Potential 3D point clusters belonging to arms and feet are extracted employing an adapted version of RANSAC based surface detection algorithm. The periodicity feature is established via a Fourier-transform based periodogram that confirms the walk periodicity for each point-cluster representing limbs. RGB or intensity data from the stereo-vision input is completely ignored and the proposed method completely relies upon 3D data produced by the stereo-vision sensor. This independence from light-based information, produces reliable illumination invariant pedestrian detection and tracking results in outdoor environment using Daimler stereo pedestrian detection dataset.

Keywords: robotics; pedestrian detection; pedestrian tracking; NARF feature-based tracking; particle filters; gait periodicity analysis; motion periodicity; stereo vision; human walking; motion modelling; vision sensors; outdoor environments; urban robots; safety; robot vision.

DOI: 10.1504/IJCAT.2014.063913

International Journal of Computer Applications in Technology, 2014 Vol.50 No.1/2, pp.113 - 121

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 25 Jul 2014 *

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