Brian E. Moore
Mubarak Shah
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Abstract
People in high-density crowds appear to move with the flow of the crowd, like particles in a liquid.
- Ali, S. and Shah, M. A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Minneapolis, June 18--23, 2007), 1--6.Google Scholar
Cross Ref
- Ali, S. and Shah, M. Floor fields for tracking in high-density crowd scenes. In Proceedings of the 10th European Conference on Computer Vision (Marseille, France, Oct. 12--18), Springer, 2008. Google ScholarDigital Library
- Andrade, E.L., Blunsden, S., and Fisher, R.B. Modeling crowd scenes for event detection. In Proceedings of the 18th International Conference of Pattern Recognition (Hong Kong, Aug. 20--24, 2006). Google ScholarDigital Library
- Bennett, A. Lagrangian Fluid Dynamics. Cambridge University Press, New York, 2006.Google ScholarCross Ref
- Brostow, G. and Cipolla, R. Unsupervised Bayesian detection of independent motion in crowds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (New York, June 17--22, 2006). Google ScholarDigital Library
- Burstedde, C., Klauck, K., Schadschneider, A., and Zittartz, J. Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A: Statistical Mechanics and its Applications 295, 3--4 (June 2001), 507--525.Google ScholarCross Ref
- Chan, A.B. and Vasconcelos, N. Bayesian poisson regression for crowd counting. In Proceedings of 12th IEEE International Conference on Computer Vision (Kyoto, Sept. 27-Oct. 4, 2009), 545--551.Google ScholarCross Ref
- Chan, A.B. and Vasconcelos, N. Mixtures of dynamic textures. In Proceedings of the 10th IEEE International Conference on Computer Vision (Beijing, Oct. 17--20, 2005), 641--647. Google ScholarDigital Library
- Helbing, D. Traffic and related self-driven many-particle systems. Review of Modern Physics 73, 4 (Dec. 2001), 1067--1141.Google ScholarCross Ref
- Helbing, D. and Molnar, P. Social force model for pedestrian dynamics. Physical Review E 51, 5 (May 1995), 4282--4286.Google ScholarCross Ref
- Hughes, R.L. The flow of human crowds. Annual Review of Fluid Mechanics 3 (2003), 169--182.Google ScholarCross Ref
- Hughes, R.L. A continuum theory for the flow of pedestrians. Transportation Research (Part B: Methodological) 36, 6 (July 2002), 507--535.Google Scholar
- Kirchner, A. and Schadschneider, A. Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A: Statistical Mechanics and its Applications 312, 1--2 (Sept. 2002), 260--276.Google ScholarCross Ref
- Kotelenez, P. Stochastic Ordinary and Stochastic Differential Equations. Springer, New York, 2008.Google Scholar
- Kratz, L. and Nishino, K. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Miami, June 20--26, 2009), 1446--1453.Google ScholarCross Ref
- Marques, J.S., Jorge, P.M., Abrantes, A.J., and Lemos, J.M. Tracking groups of pedestrians in video sequences. In Proceedings of the IEEE Computer Vision and Pattern Recognition Workshop (2003), 101.Google ScholarCross Ref
- Mehran, R., Oyama, A., and Shah, M. Abnormal behavior detection using social force model. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Miami, June 20--26, 2009), 935--942.Google ScholarCross Ref
- Pellegrini, S., Ess, A., Schindler, K., and van Gool, L. You'll never walk alone: Modeling social behavior for multi-target tracking. In Proceedings of the 12
th IEEE International Conference on Computer Vision (Kyoto, Sept. 27-Oct. 4, 2009).Google ScholarCross Ref
- Reisman, P., Mano, O., Avidan, S., and Shashua, A. Crowd detection in video sequences. In Proceedings of the IEEE Intelligent Vehicles Symposium (Parma, Italy, June 14--17, 2004), 66--71.Google Scholar
- Sand, P. and Teller, S. Particle video: Long-range motion estimation using point trajectories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (New York, June 17--22, 2006), 2195--2202. Google ScholarDigital Library
- Shadden, S.C., Lekien, F., and Marsden, J.E. Definition and properties of Lagrangian coherent structures from finite time Lyapunov exponents in two-dimensional aperiodic flows. Physica D: Nonlinear Phenomena 212, 3--4 (Dec. 2005), 271--304.Google ScholarCross Ref
- Tu, P., Sebastian, T., Doretto, G., Krahnstoever, N., Rittscher, J., and Yu, T. Unified crowd segmentation. In Proceedings of the 10
th European Conference on Computer Vision (Marseille, Oct. 12--18, 2008), 691--704.Google ScholarCross Ref
- Yilmaz, A., Javed, O., and Shah, M., Object tracking: A survey. ACM Computing Surveys 38, 4 (2006), 13.1--13.45. Google ScholarDigital Library
- Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., and Xu, L. Crowd analysis: A survey. Machine Vision and Applications 19, 5--6 (2008), 345--357. Google ScholarDigital Library
- Zhao, T. and Nevatia, R. Tracking multiple humans in a crowded environment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Washington, D.C., June 27--July 2, 2004), II-406--II-413. Google ScholarDigital Library
Index Terms
- Visual crowd surveillance through a hydrodynamics lens
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