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Proceedings Paper

Cumulative frame differencing for urban vehicle detection
Author(s): Ma'moun Al-Smadi; Khairi Abdulrahim; Rosalina Abdul Salam
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Paper Abstract

Motion segmentation is a fundamental step for vehicle detection especially in urban traffic surveillance systems. Temporal frame differencing is the simplest and fastest technique that is used to identify foreground moving vehicles from static background scene. Conventional techniques utilize background modelling and subtraction, which involves poor adaptation under slow or temporarily stopped vehicles. To address this problems cumulative frame differencing (CFD) is proposed. Dynamic threshold value based on the standard deviation of CFD is used to estimate global variance of the motion accumulated variations of pixel intensity. The tests of the proposed technique achieve robust and accurate vehicle segmentation, which improves detection of slow motion, temporary and long term stopped vehicles, moreover, it enables the real-time capability.

Paper Details

Date Published: 11 July 2016
PDF: 7 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100110G (11 July 2016); doi: 10.1117/12.2242959
Show Author Affiliations
Ma'moun Al-Smadi, Univ. Sains Islam Malaysia (Malaysia)
Khairi Abdulrahim, Univ. Sains Islam Malaysia (Malaysia)
Rosalina Abdul Salam, Univ. Sains Islam Malaysia (Malaysia)

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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