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Proceedings Paper

Unattended vehicle detection for automatic traffic light control
Author(s): Aya Salama Abdel Hady; Mohamed Moustafa
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Paper Abstract

Machine vision based traffic light control depends mainly on measuring traffic statistics at cross roads. Most of the previous studies have not taken unattended vehicles into consideration when calculating either the traffic density or the traffic flow. In this paper, we propose incorporating unattended vehicles into a new metric for measuring the traffic congestion. In addition to the vehicle motion analysis, opening the driver's side door is an important indicator that this vehicle is going to be unattended. Therefore, we focus in this paper on presenting how to detect this event for stationary vehicles from a live camera or a video feed. Through a set of experiments, we have found out that a Scale Invariant Feature Transform (SIFT) feature-descriptor with a Support Vector Machines (SVM) classifier was able to successfully classify open-door vehicles from closed-door ones in 96.7% of our test dataset.

Paper Details

Date Published: 24 December 2013
PDF: 5 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670W (24 December 2013); doi: 10.1117/12.2050938
Show Author Affiliations
Aya Salama Abdel Hady, American Univ. in Cairo (Egypt)
Mohamed Moustafa, American Univ. in Cairo (Egypt)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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