Abstract
Empirical data of human crowd behaviors are indispensable for the further understanding of pedestrian dynamics. In this paper, we describe a technique for the semi-automatic extraction of pedestrian trajectories from video recordings of human crowds. This method works on data obtained from an arbitrary observation angle and does not require additional information like the heights of the pedestrians etc. It is thus suitable for the analysis of data that have not been specifically prepared for this purpose, such as surveillance videos. We employ this method to analyze video recordings from a series of experiments that we conducted last year to reproduce pedestrian flows under controlled conditions. From these data we also estimate the continuous density of these pedestrian flows via a nearest-neighbor kernel density method which we argue is particularly suited for particle densities in general and human crowds consisting of multiple populations in particular.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boltes, M., Seyfried, A., Steffen, B., Schadschneider, A.: Automatic extraction of pedestrian trajectories from video recordings. In: Pedestrian and Evacuation Dynamics 2008, pp. 43–54 (2010)
Burstedde, C., Klauck, K., Schadschneider, A., Zittartz, J.: Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A 295, 507–525 (2001)
Chen, M.-J., Bärwolff, G., Schwandt, H.: Automaton model with variable cell size for the simulation of pedestrian flow (2008), An electronic version can be retrieved at: http://www.math.tu-berlin.de/~chenmin/pub/cbs080331.pdf (accessed June 17, 2011)
Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Pattern Analysis and Machine Intelligence 25(2), 281–288 (2003)
Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: Proc. ICCV, pp. 438–445 (2001)
Daamen, W., Bovy, P.H.L., Hoogendoorn, S.P.: Modelling pedestrians in transfer stations. In: Pedestrian and Evacuation Dynamics, pp. 59–73 (2002)
Helbing, D.: Verkehrsdynamik: Neue physikalische Modellierungskonzepte. Springer, Heidelberg (1997)
Helbing, D.: Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067–1141 (2001)
Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407, 487–490 (2000)
Helbing, D., Johansson, A., Al-Abideen, H.Z.: Dynamics of crowd disasters: An empirical study. Phys. Rev. E 75, 046109 (2007)
Huth, F., Bärwolff, G., Schwandt, H.: An extended multi-phase transport model for pedestrian flow (in preparation, 2011)
Johansson, A.F.: Data-driven modeling of pedestrian crowds. PhD thesis, Technische Universität Dresden (2008)
Kraus, K.: Photogrammetry. In: Geometry from Images and Laser Scans. de Gruyter, Berlin (2007)
Schadschneider, A.: Cellular automaton approach to pedestrian dynamics - theory. In: Pedestrian and Evacuation Dynamics, pp. 75–85 (2002)
Schadschneider, A., Seyfried, A.: Empirical results for pedestrian dynamics and their implications for cellular automata models. In: Timmermans, H. (ed.) Pedestrian Behavior: Data Collection and Applications, pp. 27–43. Imprint Emerald Group Publishing Ltd. (2009)
Shi, J., Tomasi, C.: Good features to track. In: Proc. of the IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, Boca Raton (1986)
Steffen, B., Seyfried, A.: Methods for measuring pedestrian density, flow, speed and direction with minimal scatter. Physica A 389, 1902–1910 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Plaue, M., Chen, M., Bärwolff, G., Schwandt, H. (2011). Trajectory Extraction and Density Analysis of Intersecting Pedestrian Flows from Video Recordings. In: Stilla, U., Rottensteiner, F., Mayer, H., Jutzi, B., Butenuth, M. (eds) Photogrammetric Image Analysis. PIA 2011. Lecture Notes in Computer Science, vol 6952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24393-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-24393-6_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24392-9
Online ISBN: 978-3-642-24393-6
eBook Packages: Computer ScienceComputer Science (R0)