CUS  Vol.3 No.1 , March 2015
Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009
Abstract: In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousands of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the estimate. In this work we present a real-time method for pedestrian recognition and tracking in sequences of high resolution images obtained by a stationary (high definition) camera located in different places on the Haram mosque in Mecca. The objective is to estimate pedestrian velocities as a function of the local density. The resulting data of tracking moving pedestrians based on video sequences are presented in the following section. Through the evaluated system the spatiotemporal coordinates of each pedestrian during the Tawaf ritual are established. The pilgrim velocities as function of the local densities in the Mataf area (Haram Mosque Mecca) are illustrated and very precisely documented. Tracking in such places where pedestrian density reaches 7 to 8 persons/m2 is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The tracking method which is outlined in this paper overcomes these challenges by using a virtual camera which is matched in position, rotation and focal length to the original camera in such a way that the features of the 3D-model match the feature position of the filmed mosque. In this model an individual feature has to be identified by eye, where contrast is a criterion. We do know that the pilgrims walk on a plane, and after matching the camera we also have the height of the plane in 3D-space from our 3D-model. A point object is placed at the position of a selected pedestrian. During the animation we set multiple animation-keys (approximately every 25 to 50 frames which equals 1 to 2 seconds) for the position, such that the position of the point and the pedestrian overlay nearly at every time. By combining all these variables with the available appearance information, we are able to track individual targets in high density crowds.
Cite this paper: Dridi, M. (2015) Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009. Current Urban Studies, 3, 35-53. doi: 10.4236/cus.2015.31005.

[1]   Boltes, M., Seyfried, A., Steffen, B., & Schadschneider, A. (2010). Automatic Extraction of Pedestrian Trajectories from Video Recordings. In W. W. F. Klingsch, C. Rogsch, A. Schadschneider, & M. Schreckenberg (Eds.), Pedestrian and Evacuation Dynamics 2008 (pp. 43-54). Heidelberg: Springer Berlin Heidelberg.

[2]   Bulpitt, A. J., & Sumpter, N. (1998). Learning Spatio-Temporal Patterns for Predicting Object Behaviour. Proceedings of the 9th British Machine Vision Conference (BMVC), Southampton, 649-658.

[3]   Chattaraj, U., Seyfried, A., & Chakroborty, P. (2009). Comparison of Pedestrian Fundamental Diagram across Cultures. Advances in Complex Systems, 12, 393.

[4]   Fruin, J. J. (1987). Pedestrian Planning and Design. Mobile, AL: Elevator World, Inc.

[5]   Fruin, J. J., American Society of Mechanical Engineers, & American Society of Mechanical Engineers, Standing Committee on Transportation (1970). Designing for Pedestrians: A Level of Service Concept. Ann Arbor, MI: University Microfilms, Inc.

[6]   Heisele, B., & Woehler, C. (1998). Motion-Based Recognition of Pedestrians. Proceedings of 14th International Conference on Pattern Recognition, Brisbane, 16-20 August 1998, 1325-1330.

[7]   Helbing, D., Al-Abideen, H. Z., & Johansson, A. (2007). The Dynamics of Crowd Disasters: An Empirical Study. Physical Review E, 75, Article ID: 046109.

[8]   Hoffman, D. D., & Flinchbaugh, B. E. (1982). The Interpretation of Biological Motion. Biological Cybernetics, 42, 195- 204.

[9]   Hoogendoorn, S. P., & Daamen, W. (2005). Pedestrian Behavior at Bottlenecks. Transportation Science, 39, 147-159.

[10]   Johansson, A., Al-Abideen, H. Z., Al-Bosta, S., & Helbing, D. (2008). From Crowd Dynamics to Crowd Safety: A Video-Based Analysis. Advances in Complex Systems, 11, 497-527.

[11]   Knoblauch, R. L., Pietrucha, M. T., & Nitzburg, M. (1996). Field Studies of Pedestrian Walking Speed and Start-Up Time (pp. 27-38). Transportation Research Record 1538, Washington DC: National Research Council, Transportation Research Board.

[12]   Lam, W. H. K., & Cheung, C. Y. (2000). Pedestrian Speed/Flow Relationships for Walking Facilities in Hong Kong. Journal of Transportation Engineering, ASCE, 126, 343-349.

[13]   Li, X., Shen, L., & Li, H. (2006). Estimation of Crowd Density Based on Wavelet and Support Vector Machine. Transactions of the Institute of Measurement and Control (London), 28, 299-308.

[14]   Marana, A. N., Costa, L. F., Lotufo, R. A., & Velastin, S. A. (1998). On the Efficacy of Texture Analysis for Crowd Monitoring. Proceedings of SIBGRAPI’98. International Symposium on Computer Graphics, Image Processing, and Vision, Rio de Janeiro, 20-23 October 1998, 354-361.

[15]   Masoud, O., & Papanikolopoulos, N. P. (2001). A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera. IEEE Transactions on Vehicular Technology, 50, 1267-1278.

[16]   Murakami, S., & Wada, A. (2000). An Automatic Extraction and Display Method of Walking Persons’ Trajectories. Proceedings of 15th International Conference on Pattern Recognition, 4, 611-614.

[17]   O’Flaherty, C. (1996). Transport Planning and Traffic Engineering. Engineering Village. Oxford: Taylor & Francis.

[18]   Papageorgiou, C., & Poggio, T. (1999). Trainable Pedestrian Detection. Proceedings of 1999 International Conference on Image Processing, 4, 35-39.

[19]   Predtetschenski, W. M., & Milinski, A. I. (Russ: 1969, Germ: 1971). Personenstr?me in Geb?uden. Berlin: Staatsverlag der Deutschen Demokratischen Republik.

[20]   Ricquebourg, Y., & Bouthemy, P. (2000). Real-Time Human Figure Control Using Tracked Blobs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 220, 797-808.

[21]   Sen, G., Wei, L., & Ping, Y. H. (2009). Counting People in Crowd Open Scene Based on Grey Level Dependence Matrix. IEEE International Conference on Information and Automation, Zhuhai, 22-24 June 2009, 228-231.

[22]   Seyfried, A., Steffen, B., Klingsch, W., & Boltes, M. (2005). The Fundamental Diagram of Pedestrian Movement Revisited. Journal of Statistical Mechanics, 2005, P10002.

[23]   Tsuchikawa, M., Sato, A., Koike, H., & Tomono, A. (1995). A Moving-Object Extraction Method Robust against Illumination Level Changes for a Pedestrian Counting System. Proceedings of International Symposium on Computer Vision, 563-568.

[24]   Verona, V., & Marana, A.N. (2001). Wavelet Packet Analysis for Crowd Density Estimation. Proceedings of the Listed International Symposium on Applied Information, Innsbmck: Aeta Press, 535-540.

[25]   Weidmann, U. (1993). Transporttechnik der fussg?nger. Technical Report Schriftenreihe des IVT Nr. 90, Zürich: Institut für Verkehrsplanung, Transporttechnik, Strassen-und Eisenbahnbau, ETH Zürich, Zweite, erg?nzte Auflage.

[26]   Wu, X. (2006). Crowd Density Estimation Using Texture Analysis and Learning. IEEE International Conference on Robotics and Biomimetics, Kunming, 17-20 December 2006, 214-219.

[27]   Zhang, Z., & Li, M. (2012). Crowd Density Estimation Based on Statistical Analysis of Local Intra-Crowd Motions for Public Area Surveillance. Optical Engineering, 51, Article ID: 047204.