Robust Vehicle and Traffic Information Extraction for Highway Surveillance

Autor: Yeh Chia-Hung, Kuo C-CJay, Yoneyama Akio
Jazyk: angličtina
Rok vydání: 2005
Předmět:
Zdroj: EURASIP Journal on Advances in Signal Processing, Vol 2005, Iss 14, p 912501 (2005)
Druh dokumentu: article
ISSN: 1687-6172
1687-6180
DOI: 10.1155/ASP.2005.2305
Popis: A robust vision-based traffic monitoring system for vehicle and traffic information extraction is developed in this research. It is challenging to maintain detection robustness at all time for a highway surveillance system. There are three major problems in detecting and tracking a vehicle: (1) the moving cast shadow effect, (2) the occlusion effect, and (3) nighttime detection. For moving cast shadow elimination, a 2D joint vehicle-shadow model is employed. For occlusion detection, a multiple-camera system is used to detect occlusion so as to extract the exact location of each vehicle. For vehicle nighttime detection, a rear-view monitoring technique is proposed to maintain tracking and detection accuracy. Furthermore, we propose a method to improve the accuracy of background extraction, which usually serves as the first step in any vehicle detection processing. Experimental results are given to demonstrate that the proposed techniques are effective and efficient for vision-based highway surveillance.
Databáze: Directory of Open Access Journals
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