Robust traffic event extraction from surveillance video

Akio Yoneyama; Chia-Hung Yeh; Chung-Chieh Jay Kuo

doi:10.1117/12.528445

18 January 2004 Robust traffic event extraction from surveillance video

Akio Yoneyama, Chia-Hung Yeh, Chung-Chieh Jay Kuo

Proceedings Volume 5308, Visual Communications and Image Processing 2004; (2004) https://doi.org/10.1117/12.528445
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

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.

Citation Download Citation

Akio Yoneyama, Chia-Hung Yeh, and Chung-Chieh Jay Kuo "Robust traffic event extraction from surveillance video", Proc. SPIE 5308, Visual Communications and Image Processing 2004, (18 January 2004); https://doi.org/10.1117/12.528445

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