Using Adaptive Kalman Predictor to Improve the Performance of Image-Based Traffic Monitoring System

Image processing is an important technology to collect real-time traffic data in intelligent transportation system. This paper implements an approach for detecting traffic parameters by means of feature-based reasoning on visual data, and tries to track and classify traffic objects with predefined features. An adaptive Kalman prediction algorithm is presented to improve the prediction accuracy of location, and is compared with standard Kalman Predictor algorithm on computation complexity. Simulated experimental and factual experimental results are also shown to demonstrate the effectiveness of the proposed algorithm.

• Corporate Authors:

  ITS America

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  Washington, DC  United States  20036
• Authors:
  • Qiu, Zhijun
  • Yao, Danya
  • Ban, Xuegang (Jeff)
  • Ran, Bin
• Conference:
  • 12th World Congress on Intelligent Transport Systems
  • Location: San Francisco California, United States
  • Date: 2005-11-6 to 2005-11-10
• Publication Date: 2005

Language

• English

Media Info

• Media Type: Print
• Features: Appendices; CD-ROM; Figures; References; Tables;
• Pagination: 12p
• Monograph Title: Proceedings of the 12th World Congress on Intelligent Transport Systems

Subject/Index Terms

• TRT Terms: Algorithms; Data collection; Experiments; Incident detection; Intelligent transportation systems; Kalman filtering; Real time information; Traffic; Traffic congestion; Travel demand; Visualization
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01015741
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 31 2006 9:56AM