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Robust Vehicle and Traffic Information Extraction for Highway Surveillance

Abstract

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.

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Correspondence to Akio Yoneyama.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Yoneyama, A., Yeh, CH. & Kuo, CC.J. Robust Vehicle and Traffic Information Extraction for Highway Surveillance. EURASIP J. Adv. Signal Process. 2005, 912501 (2005). https://doi.org/10.1155/ASP.2005.2305

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Keywords

  • traffic monitoring
  • object tracking
  • moving cast shadow
  • occlusion
  • nighttime detection
  • background subtraction