A State-of-the-Art Review

Abstract

In this chapter we present the related work identified with the main goal of this monograph. In crowds, cultural and personality influence can be considered in attributes such as interpersonal spaces, speeds of pedestrians, collision avoidance, and formation of groups of people in the crowd, among others. Several researches by Zhan et al. (MVA 19(5–6):345–357, 2008), Weina et al. (IEEE Trans Pattern Anal Mach Intell 34(5):1003–1016, 2012), Solmaz et al. (IEEE Trans Pattern Anal Mach Intell 34(10):2064–2070, 2012), Chandran et al. (Identifying social groups in pedestrian crowd videos. In: ICAPR, pp 1–6, 2015) focus on group identification through computational vision for information extraction. Some group detection and behavior work are presented in Sect. 4.1. In addition to information about individuals and their groups, fundamental diagrams can also be used to infer culture. The fundamental diagrams describe the relationship between velocity, density, and flow of individuals in multitudes, as described in Sect. 2.6 . Section 4.2 presents some work that use FDs in crowds by Cao et al. (Physica A Stat Mech Appl 506:661–670, 2018), Helbing et al. (Phys Rev E Stat Nonlin Soft Matter Phys 75(4 Pt 2):046, 109, 2007), Seyfried et al. (Enhanced empirical data for the fundamental diagram and the flow through bottlenecks. In: Pedestrian and evacuation dynamics 2008. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 145–156, 2010), Wolinski et al. (Comput Graph Forum 33(2):303–312, 2014), Narang et al. (PLoS One 10(4):1–17, 2015), Best et al. (Densesense: interactive crowd simulation using density-dependent filters. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation, SCA ’14. Eurographics Association, Aire-la-Ville, Switzerland, pp 97–102, 2015). Section 4.3 shows some research about personality by Goldberg (From ace to zombie: some explorations in the language of personality, chap 6. Lawrence Erlbaum Associates, Hillsdale, pp 203–234, 1982) and emotion traits by Ortony et al. (The cognitive structure of emotions. Cambridge University Press, New York, 1990). Finally, Sect. 4.4 addresses some work that seek to extract or simulate cultural aspects by Hofstede et al. (Cultures and organizations: software of the mind, vol 2. McGraw-Hill, London, 1991) in crowds by Chattaraj et al. (Adv Complex Syst 12(03):393–405, 2009), Guy et al. (Simulating heterogeneous crowd behaviors using personality trait theory. In: Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’11. ACM, New York, pp 43–52, 2011), Lala et al. (Towards a virtual environment for capturing behavior in cultural crowds. In: 2011 sixth international conference on digital information management, Melbourne, Australia, pp 310–315, 2011), Kaminka and Fridman (Simulating urban pedestrian crowds of different cultures. Tech. Rep. 3, 2018). Also the work performed by Sorokowska et al. (J Cross Cult Psychol 48:577–592, 2017), which investigate the preferred distance that a pedestrian keeps from others in several countries.