Gmm Based Vehicle Traffic Analysis On Roads

Gmm Based Vehicle Traffic Analysis On Roads

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-45 Number-3
Year of Publication : 2017
Authors : Ch.Sravani, A.Janardhana, M.Varalakshmi, Y.Kyathi
DOI :  10.14445/22315381/IJETT-V45P223

Citation 

Ch.Sravani , A.Janardhana, M.Varalakshmi, Y.Kyathi " Gmm Based Vehicle Traffic Analysis On Roads ", International Journal of Engineering Trends and Technology (IJETT), V45(3),103-107 March 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
This paper presents a complete idea for analysing the behaviour of vehicles in the real-time traffic monitoring applications. Detecting and counting cars can be used to analyse traffic patterns. Detection is the first step prior to performing more sophisticated tasks such as tracking of vehicles by their type. Receiving the images through video surveillance camera in the first phase, we get use of Gaussian mixture model for each frame to achieve a precise background image. This phase is called training phase where the geometrical structure of the road is analysed. In the second phase, the received images will be compared with the trained images. Thus, the vehicles can be tracked. In third phase, a green block will surround each vehicle to enable the researches count them. With a view to do improvements, it is proposed to develop a unique algorithm for vehicle data recognition and tracking using Gaussian mixture model.

 References

[1] Takashi morimoto, Osama kiriyama, youmei harada,Object tracking in video images based on image segmentation and pattern matching IEEE conference proceedings, vol no 05, page no, 3215-3218, 2005
[2] Zehang, S., Bebis, G., Miller, R.: On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 694–711 (2006)
[3] Bin, D., Yajun, F., Tao,W.: A vehicle detection method via symmetry in multi-scale windows. In: 2nd IEEE Conference on Industrial Electronics and Applications, 2007 (ICIEA 2007), pp. 1827–1831 (2007)
[4] Nedevschi, S., Vatavu, A., Oniga, F., Meinecke, M.M.: Forward collision detection using a stereo vision system. In: 4th International Conference on Intelligent Computer Communication and Processing, 2008 (ICCP 2008), pp. 115–122 (2008)
[5] Reynolds, D.A., Rose, R.C.: Robust Text-Independent Speaker Identification using Gaussian Mixture Speaker Models. IEEE Transactions on Acoustics, Speech, and Signal Processing 3(1) (1995) 72–83
[6] Boeing, A., Braunl, T.: ImprovCV: Open component based automotive vision. In: Intelligent Vehicles Symposium, 2008 IEEE, pp. 297–302 (2008)
[7] Junqiu wang [Member IEEE], yasushi yagi [Member IEEE],?Integrating colour and shape texture features for adaptive real time object tracking?, IEEE transactions on image processing, vol no 17,no 2, page no 235-240,2007

Keywords
Detection, surveillance, Gaussian mixture model, tracking