ICIC 2014: Intelligent Computing Methodologies pp 310-317 | Cite as
Augmented Reality Surveillance System for Road Traffic Monitoring
Abstract
This paper introduces the augmented reality surveillance system which evaluates the density of the traffic on roads and displays information in an easy to understand form over the video stream and a map. A mutual dependence between the real world, global coordinates and the position of the pixel in the image is explained. The way to find the real size of an object by knowing its dimension in the image is introduced. An operator can decide what points on the map it is required to survey, and the camera will know how to rotate to those points by mapping of global coordinates to pan and tilt angles. The density of the traffic is evaluated by processing video data and applying the knowledge about real width and length of cars.
Keywords
Traffic monitoring Car detection PTZ camera Camera measurementsPreview
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References
- 1.Tyagi, V.: Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics. IEEE Transactions on Intelligent Transportation Systems 13(3), 1156–1166 (2012)CrossRefGoogle Scholar
- 2.Mao, R.X.: Road traffic density estimation in vehicular networks. In: Wireless Communications and Networking Conference, Shanghai, pp. 4653–4658 (2013)Google Scholar
- 3.Padiath, A., Vanajakshi, L., Subramanian, S.C., Manda, H.: Prediction of traffic density for congestion analysis under Indian traffic conditions. In: 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis (2009)Google Scholar
- 4.Purusothaman, S.B.: Vehicular traffic density state estimation using Support Vector Machine. In: International Conference on Emerging Trends in Computing, Communication and Nanotechnology, Tirunelveli, pp. 782–785 (2013)Google Scholar
- 5.Royani, T., Haddadnia, J., Pooshideh, M.R.: A simple method for calculating vehicle density in traffic images. In: Machine Vision and Image Processing, 6th Iranian, Isfahan (2010)Google Scholar
- 6.Kim, T., Jo, K.H.: Real-time detection of moving objects from shaking camera based on the multiple background model and temporal median background model. Domestic Journal of Institute of Control, Robotics and Systems 16(3) (2010)Google Scholar
- 7.Filonenko, A., Jo, K.H.: Visual surveillance with sensor network for accident detection, Industrial Electronics Society. In: IECON 2013 - 39th Annual Conference of the IEEE, Vienna, pp. 5516–5521 (2013)Google Scholar
- 8.Kim, J.W., Kim, T.H., Jo, K.H.: Traffic road line detection based on the vanishing point and contour information. In: 2011 Proceedings of SICE Annual Conference (SICE), Tokyo, pp. 495–498 (2011)Google Scholar
- 9.Lai, A.H.S., Yung, N.H.C.: A fast and accurate scoreboard algorithm for estimating stationary backgrounds in an image sequence. In: Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS 1998, Monterey, vol. 4, pp. 241–244 (1998)Google Scholar
- 10.Cui, X., Huang, J., Zhang, S. N., Metaxas, D.N.: Background subtraction using low rank and group sparsity constraints. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 612–625. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 11.Sundaram, N., Brox, T., Keutzer, K.: Dense point trajectories by GPU-accelerated large displacement optical flow. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 438–451. Springer, Heidelberg (2010)CrossRefGoogle Scholar