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Original Articles

A novel framework for automated monitoring and analysis of high density pedestrian flow

ORCID Icon, &
Pages 585-597 | Received 18 Jul 2018, Accepted 11 Jul 2019, Published online: 11 Sep 2019

References

  • Alahi Kawsar, L., Abdul Ghani, N., Abdulbasah Kamil, A., & Mustafa, A. (2019). Optimization based controlled evacuation. Journal of Intelligent Transportation Systems, 23(1), 1–22. doi:10.1080/15472450.2018.1562348
  • Baqui, M., & Löhner, R. (2019). Pedpiv: Pedestrian velocity extraction from particle image velocimetry. IEEE Transactions on Intelligent Transportation Systems.
  • Boltes, M., Seyfried, A., Steffen, B., & Schadschneider, A. (2010). Automatic extraction of pedestrian trajectories from video recordings. In Pedestrian and evacuation dynamics 2008 (pp. 43–54). Berlin: Springer.
  • Daamen, W., & Bovy, P. (2011). Controlled experiments to derive walking behaviour. Delft: Delft University Press.
  • Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (vol. 1, pp. 886–893).
  • Dambalmath, P., Muhammad, B., Haug, E., & Löhner, R. (2016). Fundamental diagrams for specific very high density crowds. In Proc. pedestrian and evacuation dynamics (pp. 6–11).
  • Davidich, M., & Eugene, P. (2016). Measuring pedestrian density for dense and sparse crowds with high resolution. In Pedestrian and evacuation dynamics 2016 (pp. 583–586). Hefei: University of Science and Technology of China Press.
  • Dehghan, A., Idrees, H., Zamir, A. R., & Shah, M. (2014). Automatic detection and tracking of pedestrians in videos with various crowd densities. In Pedestrian and evacuation dynamics 2012 (pp. 3–19). Cham: Springer.
  • Dollár, P. (2013). Piotr’s image and video Matlab Toolbox (PMT),https://pdollar.github.io/toolbox/.
  • Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4), 743–761. doi:10.1109/TPAMI.2011.155
  • Dridi, M. (2015). Tracking individual targets in high density crowd scenes analysis of a video recording in hajj 2009. Current Urban Studies, 03(01), 35–53. doi:10.4236/cus.2015.31005
  • Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005). Self-organized pedestrian crowd dynamics: Experiments, simulations and design solution. Transportation Science, 39(1), 1–24. doi:10.1287/trsc.1040.0108
  • Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282–8286. doi:10.1103/PhysRevE.51.4282
  • Helbing, D., & Mukerji, P. (2012). Crowd disasters as systemic failures: Analysis of the love parade disaster. EPJ Data Science, 1(1), 7. doi:10.1140/epjds7
  • Idrees, H., Saleemi, I., Seibert, C., & Shah, M. (2013). Multi-source multi-scale counting in extremely dense crowd images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2547–2554).
  • Isenhour, M. L. (2016). Simulating Occupant Response to Emergency Situations (Unpublished Doctoral Dissertation). George Mason University.
  • Isenhour, M. L., & Löhner, R. (2014). Verification of a pedestrian simulation tool using the nist recommended test cases. Transportation Research Procedia, 2, 237–245.
  • Itseez (2015). Open Source Computer Vision Library. https://github.com/itseez/opencv.
  • Johansson, A., Batty, M., Hayashi, K., Al Bar, O., Marcozzi, D., & Memish, Z. A. (2012). Crowd and environmental management during mass gatherings. The Lancet Infectious Diseases, 12(2), 150–156. doi:10.1016/S1473-3099(11)70287-0
  • Kneip, L., Scaramuzza, D., & Siegwart, R. (2011). A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation. In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2969–2976).
  • Lakoba, T. I., Kaup, D. J., & Finkelstein, N. M. (2005). Modifications of the Helbing-Molnar-Farkas-Vicsek social force model for pedestrian evolution. Simulation, 81(5), 339–352. doi:10.1177/0037549705052772
  • Löhner, R. (2010). On the modeling of pedestrian motion. Applied Mathematical Modelling, 34(2), 366–382. doi:10.1016/j.apm.2009.04.017
  • Lohner, R., Baqui, M., Haug, E., & Muhamad, B. (2016). Real-time micro-modelling of a million pedestrians. Engineering Computations, 33(1), 217–237. doi:10.1108/EC-02-2015-0036
  • Löhner, R., Haug, E., Zinggerling, C., & Onate, E. (2016). Real-time micro-modeling of city evacuations. In Proc. pedestrian and Evacuation Dynamics (pp. 500–504).
  • Ma, J., Song, W., Lo, S. M., & Fang, Z. (2013). New insights into turbulent pedestrian movement pattern in crowd-quakes. Journal of Statistical Mechanics: Theory and Experiment, 2013(02), P02028. doi:10.1088/1742-5468/2013/02/P02028
  • Ma, Y., Lee, E. W. M., & Yuen, R. K. K. (2016). An artificial intelligence-based approach for simulating pedestrian movement. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3159–3170. doi:10.1109/TITS.2016.2542843
  • Ma, Z., & Chan, A. B. (2013). Crossing the line: Crowd counting by integer programming with local features. IEEE Conference on Computer Vision and Pattern Recognition, 1063-69/13 (pp. 2535–2546).
  • Maurin, B., Masoud, O., & Papanikolopoulos, N. P. (2005). Tracking all traffic: Computer vision algorithms for monitoring vehicles, individuals, and crowds. IEEE Robotics & Automation Magazine, 12(1), 29–36. doi:10.1109/MRA.2005.1411416
  • Nedevschi, S., Bota, S., & Tomiuc, C. (2009). Stereo-based pedestrian detection for collision-avoidance applications. IEEE Transactions on Intelligent Transportation Systems, 10(3), 380–391. doi:10.1109/TITS.2008.2012373
  • Predtechenskii, V., & Milinskii, A. (1978). Planning for foot traffic flow in buildings. National Bureau of Standards, US Department of Commerce, and the National Science Foundation, Washington, DC.
  • Prince, S. (2012). Computer vision: Models learning and inference. New York: Cambridge University Press.
  • Tordeux, A., Chraibi, M., Seyfried, A., & Schadschneider, A. (2019). Prediction of pedestrian dynamics in complex architectures with artificial neural networks. Journal of Intelligent Transportation Systems, 23(3), 1–13.
  • Vizzari, G., Manenti, L., Ohtsuka, K., & Shimura, K. (2015). An agent-based pedestrian and group dynamics model applied to experimental and real-world scenarios. Journal of Intelligent Transportation Systems, 19(1), 32–45. doi:10.1080/15472450.2013.856718
  • Zhang, J., Britto, D., Chraibi, M., Löhner, R., Haug, E., & Gawenat, B. (2014). Quantitative validation of pedflow for description of unidirectional pedestrian dynamics. Transportation Research Procedia, 2, 733–738. doi:10.1016/j.trpro.2014.09.081
  • Zhang, X. L., Weng, W. G., & Yuan, H. Y. (2012). Empirical study of crowd behavior during a real mass event. Journal of Statistical Mechanics: Theory and Experiment, 2012 (08), P08012. doi:10.1088/1742-5468/2012/08/P08012

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