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Analysis of Crowd Flow Parameters Using Artificial Neural Network

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1 Candamo, J., Shreve, M., Goldgof, D.B., Sapper, D.B., and Kasturi, R. (2010) Understanding transit scenes: A survey on human behavior-recognition algorithms. IEEE Trans. Intell. Transp. Syst. 11(1), 206–224.10.1109/TITS.2009.2030963Search in Google Scholar

2 Chen, S., Zhang, J., Li, Y., and Zhang, J. (2012) A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction. IEEE Trans. Ind. Inform. 8(1), 118–127.10.1109/TII.2011.2173202Search in Google Scholar

3 Cheng, F-C., Huang, S-C., and Ruan, S-J. (2011) Scene analysis for object detection in advanced surveillance systems using Laplacian distribution model. Syst. Man Cybern. Part C Appl. Rev. IEEE Trans. 41(5), 589–598.10.1109/TSMCC.2010.2092425Search in Google Scholar

4 Dougherty, M.S., Kirby, H.R., and Boyle, R.D. (1993) The use of neural networks to recognise and predict traffic congestion. Traffic engineering & control 34 (6).Search in Google Scholar

5 Douglas, B., and Wolfgang, C. (2011) Simulating what you see. Combining computer modelling with video analysis. Ljubljana, 15 -17.Search in Google Scholar

6 Drew, D.R (1968) Traffic flow theory and control. McGraw-Hill Inc.Search in Google Scholar

7 Efros, A., Berg, A., Mori, G., and Malik, J. (2003) Recognizing action at a distance. In Ninth IEEE International Conference on Computer Vision, pp. 726–733.10.1109/ICCV.2003.1238420Search in Google Scholar

8 Farkas, I., Helbing, D., and Vicsek, T. (2000) Simulating dynamical features of escape panic. Nature, pages 487–490.10.1038/35035023Search in Google Scholar

9 Florio, L, and Mussone, L. (1996) Neural-network models for classification and forecasting of freeway traffic flow stability. Control Engineering Practice 4(2):153–164.10.1016/0967-0661(95)00221-9Search in Google Scholar

10 Fruin, J.J. (1971) Pedestrian planning and design. Elevator World Inc., Ala., New York.Search in Google Scholar

11 Greenshilds, B. (1935) A study in highway capacity. Highway Research Board Proceedings 14:448–477.Search in Google Scholar

12 http://www.opensourcephysics.org/items/detail.cfm?ID=7365, Tracker video analysis and modelling tool.Search in Google Scholar

13 Joshan, J., Athanesious, and Suresh, P. (2012) Systematic Survey on Object Tracking Methods in Video International Journal of Advanced Research in Computer Engineering & Technology, Vol. 1, Issue 8, 242-247.Search in Google Scholar

14 Kim, H.B., and Sim, K.B. (2010) A Particular Object Tracking in an Environment of Multiple Moving Objects. IEEE International Conference on Control, Automation and Systems.10.1109/ICCAS.2010.5669674Search in Google Scholar

15 Kim, W., and Kim, C. (2012) Background subtraction for dynamic texture scenes using fuzzy colour histograms. Signal Process. Lett. IEEE 19(3), 127–130.10.1109/LSP.2011.2182648Search in Google Scholar

16 Ko, S., Soatto, D., and Estrin, D. (2010) Warping background subtraction. In 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1331–1338.10.1109/CVPR.2010.5539813Search in Google Scholar

17 Kumar, K., Parida, M., Katiyar, V.K. (2013) Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport: 1–9.10.3846/16484142.2013.818057Search in Google Scholar

18 Lee, D.S. (2005) Effective Gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–835.10.1109/TPAMI.2005.102Search in Google Scholar

19 Liang, W., and Qin, W, J.L. (2007) Study on Moving Object Tracking Algorithm in Video Images. IEEE Conference on Electronic Measurement and Instruments, pp. 810-816, July 2007.Search in Google Scholar

20 May, A., and Harmut, E. (1967) Non-integer car-following models. Highway Research Record.Search in Google Scholar

21 Moonen, B., and Forstner, W. (2011) Stampede near India shrine kills 100. New York Times, January 15, 2011.Search in Google Scholar

22 Murat, Y.S., and Baskan, O. (2006) Modelling vehicle delays at signalized junctions: artificial neural networks approach. Journal of scientific and industrial research, 65(7):558–564Search in Google Scholar

23 Musse, S.R., and Thalmann, D. (1997) A Model of Human Crowd Behaviour: Group Inter Relationship and Collision Detection Analysis. In Computer Animation and Simulations 97, Proc. Euro graphics workshop, Budapest, Springer Verlag, Wien, pp. 39-51.10.1007/978-3-7091-6874-5_3Search in Google Scholar

24 Ridder, C., Munkelt, O., and Kirchner, H. (1995) Adaptive Background Estimation and Foreground Detection Using Kalman-Filtering. Proc. Int’l Conf. Recent Advances in Mechatronics, pp. 193–199.Search in Google Scholar

25 Sahani, R., and Bhuyan, P.K. (2014) Pedestrian level of service criteria for urban off-street facilities in mid-sized cities. Transport: 1–12.10.3846/16484142.2014.944210Search in Google Scholar

26 Samuel, S., and Blackman. (2004) Multiple Hypothesis Tracking for Multiple Target Tracking. IEEE A&E Systems magazine Vol. 19, No. 1, pp. 5-18.10.1109/MAES.2004.1263228Search in Google Scholar

27 Sharma, N., Chaudhry, K., and Rao, C.C. (2005) Vehicular pollution modelling using artificial neural network technique: a review. Journal of scientific and industrial research, 64(9):637Search in Google Scholar

28 Smith, B.L., and Demetsky, M.J. (1994) Short-term traffic flow prediction: neural network approach. Transportation Research Record, 1453:98–104.Search in Google Scholar

29 Stauffer, C., and Grimson, W. (1999) Adaptive background mixture models for real-time tracking. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 246–252.10.1109/CVPR.1999.784637Search in Google Scholar

30 Underwood, R.T. (1961) Speed, volume and density relationships quality and theory of traffic flow. Yale Bureau of Highway Traffic, New Haven, pp 141–188.Search in Google Scholar

31 Wolf, P.R., and Dewitt, B.A. (2000) Elements of photogrammetry with applications in GIS, Third ed. McGraw Hill.Search in Google Scholar

32 Xiaofei, J., and Honghai, L. (2010) Advances in view-invariant human motion analysis: a review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(1), 13–24.10.1109/TSMCC.2009.2027608Search in Google Scholar

33 Zhao, L., and Thorpe, C.E. (2000) Stereo-and neural network-based pedestrian detection. IEEE Trans Intelligent Transportation System 1(3):148 154.10.1109/6979.892151Search in Google Scholar

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