Chapter
Apr 26, 2012
Pest Analysis of Traffic Peccancy Forensics Service in China
Authors: Jinwei Yu [email protected], Xiying Li [email protected], and Youting ZhaoAuthor Affiliations
Publication: ICTE 2011
Abstract
Jaywalk is the most common urban traffic violation, so an intelligent jaywalker detection method is presented in this paper. The method analyses and detects jaywalker's abnormal behavior against traffic rules. Firstly, the motion object is segmented based on the background difference method, and pedestrian is recognized from vehicle according the height-width ratio. Secondly, the pedestrian's motion path is obtained through the linear predictive tracking algorithm. Then the pedestrian motion path is simplified to the line set through linear fitting, and the motion direction is calculated. Finally, the jaywalker is detected according the angle between the pedestrian's motion direction and road direction. The experiment shows that the method can detect the jaywalker effectively in short time, and it is suitable to use in real time monitoring.
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© 2011 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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ASCE Technical Topics:
- Continuum mechanics
- Detection methods
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Forensic engineering
- Infrastructure
- Intelligent transportation systems
- Linear functions
- Mathematical functions
- Mathematics
- Methodology (by type)
- Motion (dynamics)
- Pedestrians and cyclists
- Solid mechanics
- Structural engineering
- Traffic analysis
- Traffic engineering
- Transportation engineering
- Transportation management
- Urban and regional development
- Urban areas
Authors
Affiliations
Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou 510275.E-mail: [email protected]
Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou 510275.E-mail: [email protected]
Youting Zhao
Research Center of Intelligent Transportation System, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou 510275.
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