Title: Moving vehicle detection and tracking using monocular vision

Authors: Xiaodong Miao; Shunming Li; Huan Shen; Hanquan Wang; Aijing Ma

Addresses: School of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing 210009, China ' College of energy and Power, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China ' College of energy and Power, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China ' College of energy and Power, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China ' College of energy and Power, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China

Abstract: This paper aims at real-time vehicle detection and tracking. It presents a novel comprehensive algorithm by multi-cues fusion in sequence images based on monocular vision. Firstly, the vertical symmetry of vehicle rear view is utilised to extract the Region of Interest (ROI) so as to narrow the search area. And then, the sign of underneath shadow is employed to generate hypothetical positions on which potential vehicles maybe present. Following, both image intensity and figure information are used to verify the vertical symmetry of the potential vehicle candidates. Meanwhile, Mean Shift, based on the object features' model of combine colour Histogram and Orientation Histogram (HOG), is employed to fast search the potential objects. More important, both detection and tracking are under an interactive mechanism which can dramatically improve detection efficiency. Experimental results show this approach can achieve 96.34% accurate rate and run on an average 24.27 frames per second, which satisfy the driving requirements.

Keywords: machine vision; vehicle detection; vehicle tracking; intelligent vehicles; intelligent traffic systems; moving vehicles; monocular vision; multi-cue fusion; multiple cues; image sequences.

DOI: 10.1504/IJVS.2014.063247

International Journal of Vehicle Safety, 2014 Vol.7 No.3/4, pp.425 - 439

Received: 19 Jan 2013
Accepted: 15 Apr 2013
Published online: 30 Oct 2014 *

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