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Proceedings Paper

Robust traffic event extraction from surveillance video
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Paper Abstract

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.

Paper Details

Date Published: 18 January 2004
PDF: 12 pages
Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); doi: 10.1117/12.528445
Show Author Affiliations
Akio Yoneyama, KDDI R&D Labs. Inc. (Japan)
Chia-Hung Yeh, Univ. of Southern California (United States)
Chung-Chieh Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 5308:
Visual Communications and Image Processing 2004
Sethuraman Panchanathan; Bhaskaran Vasudev, Editor(s)

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