Robust vehicle and traffic information extraction for highway surveillance

Akio Yoneyama*, Chia Hung Yeh, C. C. JayKuo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2305-2321
Number of pages17
JournalEurasip Journal on Applied Signal Processing
Issue number14
Publication statusPublished - 2005 Aug 11
Externally publishedYes


  • Background subtraction
  • Moving cast shadow
  • Nighttime detection
  • Object tracking
  • Occlusion
  • Traffic monitoring

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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