Vehicle Detection and Counting in Traffic Video Based on OpenCV

Abstract:

With the development of modern social economy, the number of vehicles in China is growing rapidly, so how to get real-time traffic parameters has a very important significance in using the limited road space, vehicle video detection method based on image processing develop rapidly. With the improvement of image processing technology and microprocessor performance, makes video-based traffic parameter detection using universal. This paper deals with the real-time traffic video, gets each frame, uses Gaussian filter denoising, marks the region of interest (ROI), apply background subtraction algorithm based on average method, get the binarization foreground image, set threshold to eliminate the moving objects whose area is too small, check the boundary of ROI to judge the moving vehicle and counting, get the results as parameters of the intelligent transportation.

• Periodical: Applied Mechanics and Materials (Volumes 361-363)
• Pages: 2232-2235
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.361-363.2232
• Online since: August 2013
• Authors: Wen Jun Wang*, Meng Gao
• Keywords: Background Subtraction Algorithm, Digital Image Processing, ROI, Vehicle Counting, Vehicle Detection

References

[1] Zhiqiang Wei, Xiaopeng Ji and Peng Wang, Real-time moving object detection for video monitoring systems [J]. Journal of Systems Engineering and Electronics, 2006, 17(4): 731-736.

DOI: 10.1016/s1004-4132(07)60007-3

[2] Dai Ke-xue, Li Guo-hui, Tu Dan, YUAN Jian, Prospects and Current Studies on Background Subtraction Techniques for Moving Objects Detection from Surveillance Video[J], Journal of Image and Graphics, 2006, 11(7): 919, 928.

[3] P. KaewTraKulPong, R. Bowden. An improved adaptive background mixture model for real-time tracking with shadow detection [A]. In: Proceedings of the 2nd European Workshop on Advanced Video-Based Surveillance Systems[C], Kingston, UK, 2001: 149-158.

DOI: 10.1007/978-1-4615-0913-4_11

[4] Kaweepap Kongkittisan, Object Speed Detection from a Video Scene, Mahidol University, Bangkok Thailand, May (2003).

[5] S.J. Julier, A skewed approach to filtering, in Proc. AeroSense: 12th Int. Symp. Aerospace/Defense Sensing, Simulation and Controls, vol. 3373, Apr. 1998, pp.54-65.

[6] Information on http: /www. opencv. org. cn.