Research on Safety Vehicle Distance Recognition Based on Stereo Vision and DSP

Abstract:

With the development of economy, there are an increasing number of cars as well as traffic accidents, thus intensifying the need to take measures to reduce traffic accidents and protect the safety of life and property. Vehicle distance is one of the most important indexes of traffic safety. The measurement of safety vehicle distance has become an increasingly hot research area of intelligent transportation. Through analyzing the basic principle of stereo vision and calibrating the parameters of the CCD sensors both inside and outside, this paper comes up with a method to measure the former vehicle distance based on stereo vision and DSP. Once the vehicle speed and distance form a non-security association, it will give a warning, and upload data or force speed-limiting. According to the different coordinates of the obtained images of the target vehicle from the left and the right sensor, this method can identify feature points, calculate distance to the target vehicle, and analyze the security of vehicle distance. The experimental results show that this method has wide measurement range, high measurement accuracy, and fast operation rate, thus it can meet the actual needs of the measurement of safe vehicle distance in intelligent transportation.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 805-810
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.805
• Online since: July 2013
• Authors: Rong Bao Chen, Ning Li, Hua Feng Xiao, Wei Hou
• Keywords: Digital Signal Processor (DSP), Image Feature, Intelligent Transportation, Safe Vehicle Distance, Stereo Vision

References

[1] R. Fang and F. Shen: Analysis and forecast of the development trend of road traffic accidents, China Safety Science and Technology, Vol 8(2012), p.142

[2] J. Xu, W. Du and H. Sun: The follower vehicle safety distance analysis, Traffic and Transportation Engineering, 2(2003), pp.102-103

[3] S. Ma, and Z. Zhang, in: Computer vision-Computational Theory and Basic Algorithm, edited by Science Press, Beijing, (1998).

[4] G. Zhang, in: Photoelectric test technology and system, edited by Beijing University of Aeronautics and Astronautics Press, Beijing, (2010).

[5] Z. Zhang: A flexible new technique for camera calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 22(2000), pp.1330-1334

DOI: 10.1109/34.888718

[6] R. Chen, and J. Peng: Study on method of measuring vehicle stereo dimension with center axis, Application Research of Computers, 3 (2008), pp.31-32

[7] L. Ma: Research on measurement of moving images based on video and method of sense, edited by Hefei University of Technology, Hefei, (2006).