Abstract
Crowd evacuation is thoroughly investigated in recent years. All efforts focus on improving safety standards of such a process. Past and latest life-threatening incidents related to evacuation procedures justify both the growing scientific interest as well as the interdisciplinary character of most research approaches. In this chapter, we describe the hardware implementation of a management system that aims at acting anticipatively against crowd congestion during evacuation. The system consists of two structural components. The first one relies on an elaborated form of the Viola et al. [55] detection and tracking algorithm, which incorporates both appearance and motion in real-time. Being supported by cameras, this algorithm realises the initialisation process. In principal, it consists of simple sum-of-pixel filters that are boosted into a strong classifier. A linear combination of these filters properly set thresholds, thus succeeding detection. The second part consists of a Cellular Automata (CA) based route estimation model. Presumable congestion in front of exits during crowd egress, leads to the prompt activation of sound and optical signals that guide pedestrians towards alternative escaping points. The CA model, as well as the tracking algorithm are implemented by means of Field Programmable Gate Array (FPGA) logic. Hardware accelerates the response of the model by exploiting the distinct feature of parallelism that CA structures inherently possess. Furthermore, implementing the model on an FPGA device takes advantage of their natural parallelism, thus reaching significant speed-ups with respect to software simulation. The incorporation of the design as a fast processing module of an embedded system dedicated to surveillance is also advantageous in terms of compactness, portability and low cost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Altera: Quartus II handbook version 13.1. Altera Corporation (2013). http://www.altera.com/literature/hb/qts/quartusii_handbook.pdf
Altshuler E, Ramos O, Nunez Y, Fernandez J, Batista-Leyva AJ, Noda C (2005) Symmetry breaking in escaping ants. Am Nat 166(6):643–649
Bandini S, Federici ML, Vizzari G (2007) Situated cellular agents approach to crowd modeling and simulation. Cybern Syst 38:729–753
Girau B, Tisserand A (1996) On-line arithmetic based reprogrammable hardware implementation of multilayer perceptron back-propagation. Technical report, Ecole Normale Superieure de Lyon
Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci USA (PNAS) 99(3):7280–7287
Brand M, Kettnaker V (2000) Discovery and segmentation of activities in video. IEEE Trans Pattern Anal Mach Intell 22:844–851
Burstedde C, Klauck K, Schadschneider A, Zittartz J (2001) Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A 295:507–525
Vihas C, Georgoudas IG, Sirakoulis GC (2013) Cellular automata incorporating follow-the-leader principles to model crowd dynamics. J Cell Autom 8(5–6):333–346
Coifman B, Beymer D, McLauchlan P, Malik J (1998) A real-time computer vision system for vehicle tracking and traffic surveillance. Transp Res: Part C 6(4):271–288
Cutler R, Davis LS (2000) Robust real-time periodic motion detection, analysis, and applications. IEEE Trans Pattern Anal Machine Intell 22:781–796
Georgoudas IG, Sirakoulis GC, Andreadis I (2007) An intelligent cellular automaton model for crowd evacuation in fire spreading conditions. In: Proceedings of the 19th IEEE international conference on tools with artificial intelligence (ICTAI 2007), vol 1, pp 36–43
Georgoudas IG, Kyriakos P, Sirakoulis GC, Andreadis I (2010) An fpga implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields. Microprocess Microsyst 34(7–8):285–300
Georgoudas IG, Sirakoulis GC, Andreadis I (2007) Modelling earthquake activity features using cellular automata. Math Comput Model 46(1–2):124–137
Georgoudas IG, Sirakoulis GC, Andreadis I (2011) An anticipative crowd management system preventing clogging in exits during pedestrian evacuation processes. IEEE Syst 5(1):129–141
Goldstone RL, Janssen MA (2005) Computational models of collective behavior. Trends Cognitive Sci 9(9):424–430
Goodrich MA Potential fields tutorial. http://www.ee.byu.edu/ugrad/srprojects/robotsoccer/papers/goodrich_potential_fields.pdf
Haldera C, Madeja L, Pietrzyka M (2014) Discrete micro-scale cellular automata model for modelling phase transformation during heating of dual phase steels. Arch Civil Mech Eng 14:96–103
Haritaoglu I, Harwood D, Davis LS (2000) W4: Real-time surveillance of people and their activities. IEEE Trans Pattern Anal Machine Intell 22(8):809–822
Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407:487–490
Howarth RJ, Buxton H (1992) Analogical representation of space and time. Image Vis Comput 10:467–478
Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern - C: Appl Rev 34(3):334–352
Hu WHW, Tan TTT, Wang LWL, Maybank SMS (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern C 34:334–352
Hughes R (2002) A continuum theory for the flow of pedestrians. Transp Res B 36:507–535
Kilger M (1992) A shadow handler in a video-based real-time traffic monitoring system. In: Proceedings of the IEEE workshop applications of computer vision. IEEE, Palm Springs, pp 11–18
Kirchner A, Klupfel H, Nishinari K, Schadschneider A, Schreckenberg M (2003) Simulation of competitive egress behavior: comparison with aircraft evacuation data. Physica A 324:689–697
Kuno Y, Watanabe T, Shimosakoda Y, Nakagawa S (1996) Automated detection of human for visual surveillance system. In: Proceedings of the international conference on pattern recognition, pp 865–869
Li J, Yang L, Zhao D (2005) Simulation of bi-direction pedestrian movement in corridor. Physica A 354:619–628
Lo SM, Huang HC, Wang P, Yuen KK (2006) A game theory based exit selection model for evacuation. Fire Saf J 41:364–369
Lou JG, Yang H, Hu WM, Tan TN (2002) Visual vehicle tracking using an improved ekf. In: Proceedings of the Asian conference on computer vision, pp 296–301
Schultz M, Lehmann S, Fricke H (2007) A discrete microscopic model for pedestrian dynamics to manage emergency situations in airport terminals. In: Waldau N, Gattermann P, Knoflacher H, Schreckenberg M (eds) Pedestrian andevacuation dynamics 2005. Springer, Berlin, pp 369–375
Mackay M, Benhabib B (2007) A multi-camera active-vision system for dynamic form recognition. In: Proceedings of international conference on computer, information, and systems sciences, and engineering (CISSE2007)
Mackay M, Fenton RG, Benhabib B (2008) Time-varying-geometry object surveillance using a multi-camera active-vision system. Int J Smart Sens Intell Syst 1(3):679–704
Mackay M, Fenton RG, Benhabib B (2011) Multi-camera active surveillance of an articulated human form - an implementation strategy. Comput Vis Image Underst 115:1395–1413
McKenna S, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Underst 80(1):42–56
Mehran R (2011) Analysis of behaviors in crowd videos. Ph.D. thesis, University of Central Florida. http://crcv.ucf.edu/papers/theses/thesis-mehra005_300.pdf
Meyer D, Denzler J, Niemann H (1998) Model based extraction of articulated objects in image sequences for gait analysis. In: Proceedings of the IEEE international conference on image processing, pp 78–81
Meyer D, Denzler J, Niemann H (1998) Model based extraction of articulated objects in image sequences for gait analysis. In: Proceedings of the IEEE international conference on image processing, pp 78–81
Mita T, Kaneko T, Hori O (2005) Joint haar-like features for face detection. Multimed Lab Corp Res Dev Cent Toshiba Corp 2:1619–1626
Mohan A, Papageorgiou C, Poggio T (2001) Example-based object detection in images by components. IEEE Trans Pattern Recognit Machine Intell 23:349–361
Nalpantidis L, Sirakoulis GC, Gasteratos A (2011) Non-probabilistic cellular automata-enhanced stereo vision simultaneous localisation and mapping (slam). Meas Sci Technol 22(11):114027
Orts-Escolano S, Garcia-Rodriguez J, Morell V, Cazorla M, Azorin J, Garcia-Chamizo JM (2014) Parallel computational intelligence-based multi-camera surveillance system. J Sens Actuator Netw 3:95–112
Panagiotakis C, Tziritas G (2004) Recognition and tracking of the members of a moving human body. In: Perales FJ, Draper BA (eds) AMDO 2004, LNCS 3179, Springer, pp 86–98
Papers AW (2007) Video surveillance implementation using FPGAs. Altera Corporation. http://www.altera.com/literature/wp/wp-videosrvl.pdf
Parisi D, Dorso C (2005) Microscopic dynamics of pedestrian evacuation. Physica A 354:606–618
Recatala G, Carloni R, Melchiorri C, Sanz PJ, Cervera E, del Pobil AP (2008) Vision-based grasp tracking for planar objects. IEEE Trans Syst Man Cybern - C: Appl Rev 38(6):844–849
Remagnino P, Tan T, Baker K (1998) Agent orientated annotation in model based visual surveillance. In: Proceedings of the IEEE international conference on computer vision, pp 857–862
Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. In: Proceeding ACM SIGGRAPH ’04. ACM, pp 309–314
Roy A, Sural S (2009) A fuzzy interfacing system for gait recognition. In: Proceedings of the 28th north american fuzzy information processing society annual conference, pp 1–6
Shiwakoti N, Sarvi M, Rose G, Burd M (2011) Animal dynamics based approach for modeling pedestrian crowd egress under panic conditions. Transp Res B: Methodol 45(9):1433–1449
Tarabanis KA, Allen PK, Tsai RY (1995) A survey of sensor planning in computer vision. IEEE Trans Robot Autom 11(1):86–104
Toffoli T (1984) Cam: a high-performance cellular automaton machine. Physica D 10(1–2):195–204
Varas A, Cornejo M, Mainemer D, Toledo B, Rogan J, Munoz V, Valdivia JA (2007) Cellular automaton model for evacuation process with obstacles. Physica A 382:631–642
Velastin S, Remagnino P, (2006) Intelligent distributed video surveillance systems., IEE Computing Series, Institution of Engineering and Technology, Stevenage
Viola P, Jones M (2001) Fast and robust classification using asymmetric adaboost and a detector cascade. In: Advances in Neural Information Processing System, vol 14. MIT Press, Cambridge, pp 1311–1318
Viola P, Jones MJ, Snow D (2003) Detecting pedestrians using patterns of motion and appearance. In: Proceedings of the IEEE international conference on computer vision, pp 734–741
Vlassopoulos N, Fates NA, Berry H, Girau B (2010) An fpga design for the stochastic greenberg-hastings cellular automata. In: International conference on high performance computing & simulation - HPCS, IEEE Computer Society, pp 565–574
Wang X, Wang S, Bi D (2009) Distributed visual-target-surveillance system in wireless sensor networks. IEEE Trans Syst Man Cybern - B: Cybern 39(5):1134–1146
Yuan WF, Tan KH (2007) An evacuation model using cellular automata. Phys A 384:549–66
Zhang W, Tong R, Dong J (2009) Boosting 2-thresholded weak classifiers over scattered rectangle features for object detection. Institute of Artificial Intelligence Zhejiang University Hangzhou, pp 397–404
Zhao D, Yang L, Li J (2006) Exit dynamics of occupant evacuation in an emergency. Physica A 363:501–511
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Portokalidis, D., Georgoudas, I.G., Gasteratos, A., Sirakoulis, G.C. (2014). A Full-Scale Hardware Solution for Crowd Evacuation via Multiple Cameras. In: Spagnolo, P., Mazzeo, P., Distante, C. (eds) Human Behavior Understanding in Networked Sensing. Springer, Cham. https://doi.org/10.1007/978-3-319-10807-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-10807-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10806-3
Online ISBN: 978-3-319-10807-0
eBook Packages: Computer ScienceComputer Science (R0)