Robust traffic event extraction from surveillance video

Akio Yoneyama*, Chia H. Yeh, C. C.Jay Kuo

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


An approach to extract traffic events by integrating the low-level, middle-level, and high-level feature extraction modules is developed in this research. To be more specific, the low-level module extracts features such as motion, size, and location. The middle-level module builds a bridge between the road surface plane in the real world and the captured image plane by geometric analysis. Finally, the high-level module looks for traffic events such as "traffic jam", "lane change", and "traffic rule violation", which require the understanding of the video contents in a specific knowledge domain. In the high-level module, various traffic events are related to motion characteristics obtained from the middle-level module. It is demonstrated by experimental results that the proposed system can achieve robust traffic event extraction. The effectiveness of the proposed technique is analyzed. Conventional traffic event extraction methods demand the knowledge of capturing conditions for camera calibration. This requirement can be greatly relaxed in our proposed scheme.

Original languageEnglish
Pages (from-to)1019-1030
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue numberPART 2
Publication statusPublished - 2004
Externally publishedYes
EventVisual Communications and Image Processing 2004 - San Jose, CA, United States
Duration: 2004 Jan 202004 Jan 22


  • Image analysis
  • Object tracking
  • Projection
  • Traffic events
  • Traffic monitoring

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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