Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind
Resumo
In this work we proposed a computational model to extract pedestrian characteristics from video sequences. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. As one of the main contributions, four large dimensions of geometric characteristics (Big4GD) were proposed: I - Physical, II - Personal and Emotional, III - Social and IV - Cultural, which seek to describe the behavior of pedestrians and groups in the crowd. The GeoMind tool was developed for the purpose of detecting the four geometric dimensions from video sequences. In addition, several analyzes were carried out with the purpose of validating the proposed model, from comparing results with the literature, including the comparison of spontaneous multitudes from several countries and controlled experiments involving Fundamental Diagrams.
Referências
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W. Zhao, Z. Zhang, and K. Huang, “Gestalt laws based tracklets analysis for human crowd understanding,” Pattern Recognition, vol. 75, pp. 112–127, 2018. https://doi.org/10.1016/j.patcog.2017.06.020
W. Li, Z. Di, and J. M. Allbeck, “Crowd distribution and location preference,” Computer Animation and Virtual Worlds, vol. 23, pp. 343–351, 2012. https://doi.org/10.1002/cav.1447
U. Chattaraj, A. Seyfried, and P. Chakroborty, “Comparison of pedestrian fundamental diagram across cultures,” Advances in Complex Systems, vol. 12, pp. 393–405, 2009. https://doi.org/10.1142/S0219525909002209
G. Hofstede, Culture’s consequences: comparing values, behaviors, institutions, and organizations across nations. Thousand Oaks, CA: Sage Publications, 2001.
P. Costa and R. McCrae, Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). New York, NY: Psychological Assessment Resources, 1992.
R. R. McCrae and P. L. Costa, Toward a new generation of personality theories: Theoretical contexts for the five-factor model. New York, NY: Guilford Press, 1996, ch. 3, pp. 51–87.
A. Ortony, G. L. Clore, and A. Collins, The cognitive structure of emotions. New York, NY: Cambridge university press, 1990. https://doi.org/10.1017/CBO9780511571299
A. K. Chandran, A. P. Loh, and P. Vadakkepat, “Identifying social groups in pedestrian crowd videos,” in Eighth International Conference on Advances in Pattern Recognition (ICAPR). Kolkata, IN: IEEE, 2015, pp. 1–6. https://doi.org/10.1109/ICAPR.2015.7050677
F. Solera, S. Calderara, and R. Cucchiara, “Structured learning for detection of social groups in crowd,” in 10th IEEE International Conference on Advanced Video and Signal Based Surveillance. Krakow, PL: IEEE, 2013, pp. 7–12. https://doi.org/10.1109/AVSS.2013.6636608
A. Seyfried and A. Schadschneider, “Fundamental diagram and validation of crowd models,” in Proceedings of the 8th International Conference on Cellular Automata for Reseach and Industry. Berlin, GE: Springer-Verlag, 2008, pp. 563–566. https://doi.org/10.1007/978-3-540-79992-4_77
A. Seyfried, M. Boltes, J. Kähler, W. Klingsch, A. Portz, T. Rupprecht, A. Schadschneider, B. Steffen, and A. Winkens, Enhanced empirical data for the fundamental diagram and the flow through bottlenecks. Berlin, GE: Springer Berlin Heidelberg, 2010, ch. 11, pp. 145–156. https://doi.org/10.1007/978-3-642-04504-2_11
S. Narang, A. Best, S. Curtis, and D. Manocha, “Generating pedestrian trajectories consistent with the fundamental diagram based on physiological and psychological factors,” PLOS ONE, vol. 10, pp. 1–17, 2015. https://doi.org/10.1371/journal.pone.0117856
A. Best, S. Narang, S. Curtis, and D. Manocha, “Densesense: interactive crowd simulation using density-dependent filters,” in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Switzerland, SW: Eurographics Association, 2014, pp. 97–102. https://doi.org/10.2312/sca.20141127
D. Helbing, A. Johansson, and H. Z. Al-Abideen, “Dynamics of crowd disasters: an empirical study,” Physical Review E, vol. 75, pp. 461 091–461 097, 2007. https://doi.org/10.1103/PhysRevE.75.046109
L. Saifi, A. Boubetra, and F. Nouioua, “An approach for emotions and behavior modeling in a crowd in the presence of rare events,” Adaptive Behavior, vol. 24, pp. 428–445, 2016. https://doi.org/10.1177%2F1059712316674784
D. Lala, S. Thovuttikul, and T. Nishida, “Towards a virtual environment for capturing behavior in cultural crowds,” in Sixth International Conference on Digital Information Management. Melbourn, AU: IEEE, 2011, pp. 310–315. https://doi.org/10.1109/ICDIM.2011.6093362
N. Fridman, A. Zilka, and G. A. Kaminka, “The impact of cultural differences on crowd dynamics in pedestrian and evacuation domains,” Bar Ilan University, (Technical Report), 2011.
S. Cao, L. Lian, M. Chen, M. Yao, W. Song, and Z. Fang, “Investigation of difference of fundamental diagrams in pedestrian flow,” Physica A: Statistical Mechanics and its Applications, vol. 506, pp. 661–670, 2018. https://doi.org/10.1016/j.physa.2018.04.084
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Kauai, HI: IEEE Computer Society, 2001, pp. 511–518. https://doi.org/10.1109/CVPR.2001.990517
J. Bins, L. L. Dihl, and C. R. Jung, “Target tracking using multiple patches and weighted vector median filters,” Journal of Mathematical Imaging and Vision, vol. 45, pp. 293–307, 2013. https://doi.org/10.1007/s10851-012-0354-y
E. T. Hall, The Hidden Dimension. Garden City, NY: Doubleday, 1966.
R. M. Favaretto, L. Dihl, S. R. Musse, F. Vilanova, and A. B. Costa, “Using big-five personality model to detect cultural aspects in crowds,” in Conference on Graphics, Patterns and Images (SIBGRAPI). Niteroi, RJ: IEEE, 2017, pp. 223–229. https://doi.org/10.1109/SIBGRAPI.2017.36
R. M. Favaretto, L. Dihl, and S. R. Musse, “Detecting crowd features in video sequences,” in Proceedings of Conference on Graphics, Patterns and Images (SIBGRAPI). So Jos dos Campos, SP: IEEE Computer Society, 2016, pp. 201–208. https://doi.org/10.1109/SIBGRAPI.2016.036
P. Costa and R. R. McCrae, NEO PI-R - Inventrio de Personalidade NEO Revisado. So Paulo, SP: Vetor, 2007.
R. M. Favaretto, P. Knob, S. R. Musse, F. Vilanova, and A. B. Costa, “Detecting personality and emotion traits in crowds from video sequences,” Machine Vision and Applications, vol. 30, no. 5, pp. 999–1012, Jul 2019. https://doi.org/10.1007/s00138-018-0979-y
V. Araujo, R. Migon Favaretto, P. Knob, S. Raupp Musse, F. Vilanova, and A. Brandelli Costa, “How much do you perceive this?: An analysis on perceptions of geometric features, personalities and emotions in virtual humans,” in Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents. New York, NY, USA: ACM, 2019, pp. 179–181. https://doi.org/10.1101/622167
G. Hofstede, G. J. Hofstede, and M. Minkov, Cultures and organizations: Software of the mind. New York, NY: McGraw-Hill, 1991.
R. M. Favaretto, L. Dihl, R. Barreto, and S. R. Musse, “Using group behaviors to detect hofstede cultural dimensions,” in International Conference on Image Processing (ICIP). Phoenix, AZ: IEEE, 2016, pp. 2936–2940. https://doi.org/10.1109/ICIP.2016.7532897
F. Vilanova, F. M. Beria., A. B. Costa, and S. H. Koller, “Deindividua-tion: From le bon to the social identity model of deindividuation effects,” Cogent Psychology, vol. 4, pp. 1–21, 2017. https://doi.org/10.1080/23311908.2017.1308104
G. L. Bon, A Psicologia das Multides. Paris, FR: Presses Universitaires de France, 1986.
S. R. M. Rodolfo M. Favaretto and A. B. Costa, Emotion, Personality and Cultural Aspects in Crowds: Towards a Geometrical Mind. London, UK: Springer Nature, 2019. https://doi.org/10.1007/978-3-030-22078-5
R. Migon Favaretto, R. Rosa dos Santos, S. Raupp Musse, F. Vilanova, and A. Brandelli Costa, “Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation,” Computer Animation and Virtual Worlds, vol. 30, no. 3-4, p. e1899, 2019, e1899 cav.1899. https://doi.org/10.1002/cav.1899
P. Knob, M. Alcntara, E. Testa, R. Favaretto, G. Lima, L. Dihl, and S. R. Musse, “Generating background npcs motion and grouping behavior based on real video sequences,” Entertainment Computing, vol. 27, pp. 179–187, 2018. https://doi.org/10.1016/j.entcom.2018.06.003
V. Araujo, R. Migon Favaretto, P. Knob, S. Raupp Musse, F. Vilanova, and A. Brandelli Costa, “How much do you perceive this?: An analysis on perceptions of geometric features, personalities and emotions in virtual humans,” in Proceedings of the 19th ACMInternational Conference on Intelligent Virtual Agents, ser. IVA ’19. New York, NY, USA: ACM, 2019, pp. 179–181. https://doi.org/10.1145/3308532.3329454
M. Alcantara, E. Testa, G. L. da Silva, R. Favaretto, L. Dihl, and S. Musse, “Generating background population for games based on real video sequences,” in Brazilian Symposium on Games and Digital Entertainment (SBGames). So Paulo, SP: SBGames, 2016, pp. 66–72.
L. Dihl, E. Testa, P. Knob, G. Lima, R. M. Favaretto, M. Alcantara, and S. R. Musse, “Generating cultural characters based on hofstede dimensions,” in Virtual Humans and Crowds for Immersive Environments (VHCIE). Los Angeles, CA: IEEE, 2017, pp. 1–6. https://doi.org/10.1109/VHCIE.2017.7935621
P. Knob, V. F. de A. Araujo, R. M. Favaretto, and S. R. Musse, “Visualization of interactions in crowd simulation and video sequences,” in XVII Brazilian Symposium on Games and Digital Entertainment (SBGames). Foz do Iguau, PR: SBGames, 2018, pp. 601–610. https://doi.org/10.1109/SBGAMES.2018.00037
R. M. Favaretto, “Geomind: a software to extract emotion, personality and cultural aspects from video sequences.” 2019. [Online]. Available: https://www.rmfavaretto.pro.br/geomind
R. M. Favaretto, “Cultural crowds dataset: a set of videos from several countries,” 2018. [Online]. Available: https://www.rmfavaretto.pro.br/vhlab/datasets.php
Z. Shahhoseini and M. Sarvi, “Pedestrian crowd flows in shared spaces: investigating the impact of geometry based on micro and macro scale measures,” Transportation Research Part B: Methodological, vol. 112, pp. 57–87, 2019. https://doi.org/10.1016/j.trb.2019.01.019
W. Zhao, Z. Zhang, and K. Huang, “Gestalt laws based tracklets analysis for human crowd understanding,” Pattern Recognition, vol. 75, pp. 112–127, 2018. https://doi.org/10.1016/j.patcog.2017.06.020
W. Li, Z. Di, and J. M. Allbeck, “Crowd distribution and location preference,” Computer Animation and Virtual Worlds, vol. 23, pp. 343–351, 2012. https://doi.org/10.1002/cav.1447
U. Chattaraj, A. Seyfried, and P. Chakroborty, “Comparison of pedestrian fundamental diagram across cultures,” Advances in Complex Systems, vol. 12, pp. 393–405, 2009. https://doi.org/10.1142/S0219525909002209
G. Hofstede, Culture’s consequences: comparing values, behaviors, institutions, and organizations across nations. Thousand Oaks, CA: Sage Publications, 2001.
P. Costa and R. McCrae, Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). New York, NY: Psychological Assessment Resources, 1992.
R. R. McCrae and P. L. Costa, Toward a new generation of personality theories: Theoretical contexts for the five-factor model. New York, NY: Guilford Press, 1996, ch. 3, pp. 51–87.
A. Ortony, G. L. Clore, and A. Collins, The cognitive structure of emotions. New York, NY: Cambridge university press, 1990. https://doi.org/10.1017/CBO9780511571299
A. K. Chandran, A. P. Loh, and P. Vadakkepat, “Identifying social groups in pedestrian crowd videos,” in Eighth International Conference on Advances in Pattern Recognition (ICAPR). Kolkata, IN: IEEE, 2015, pp. 1–6. https://doi.org/10.1109/ICAPR.2015.7050677
F. Solera, S. Calderara, and R. Cucchiara, “Structured learning for detection of social groups in crowd,” in 10th IEEE International Conference on Advanced Video and Signal Based Surveillance. Krakow, PL: IEEE, 2013, pp. 7–12. https://doi.org/10.1109/AVSS.2013.6636608
A. Seyfried and A. Schadschneider, “Fundamental diagram and validation of crowd models,” in Proceedings of the 8th International Conference on Cellular Automata for Reseach and Industry. Berlin, GE: Springer-Verlag, 2008, pp. 563–566. https://doi.org/10.1007/978-3-540-79992-4_77
A. Seyfried, M. Boltes, J. Kähler, W. Klingsch, A. Portz, T. Rupprecht, A. Schadschneider, B. Steffen, and A. Winkens, Enhanced empirical data for the fundamental diagram and the flow through bottlenecks. Berlin, GE: Springer Berlin Heidelberg, 2010, ch. 11, pp. 145–156. https://doi.org/10.1007/978-3-642-04504-2_11
S. Narang, A. Best, S. Curtis, and D. Manocha, “Generating pedestrian trajectories consistent with the fundamental diagram based on physiological and psychological factors,” PLOS ONE, vol. 10, pp. 1–17, 2015. https://doi.org/10.1371/journal.pone.0117856
A. Best, S. Narang, S. Curtis, and D. Manocha, “Densesense: interactive crowd simulation using density-dependent filters,” in Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Switzerland, SW: Eurographics Association, 2014, pp. 97–102. https://doi.org/10.2312/sca.20141127
D. Helbing, A. Johansson, and H. Z. Al-Abideen, “Dynamics of crowd disasters: an empirical study,” Physical Review E, vol. 75, pp. 461 091–461 097, 2007. https://doi.org/10.1103/PhysRevE.75.046109
L. Saifi, A. Boubetra, and F. Nouioua, “An approach for emotions and behavior modeling in a crowd in the presence of rare events,” Adaptive Behavior, vol. 24, pp. 428–445, 2016. https://doi.org/10.1177%2F1059712316674784
D. Lala, S. Thovuttikul, and T. Nishida, “Towards a virtual environment for capturing behavior in cultural crowds,” in Sixth International Conference on Digital Information Management. Melbourn, AU: IEEE, 2011, pp. 310–315. https://doi.org/10.1109/ICDIM.2011.6093362
N. Fridman, A. Zilka, and G. A. Kaminka, “The impact of cultural differences on crowd dynamics in pedestrian and evacuation domains,” Bar Ilan University, (Technical Report), 2011.
S. Cao, L. Lian, M. Chen, M. Yao, W. Song, and Z. Fang, “Investigation of difference of fundamental diagrams in pedestrian flow,” Physica A: Statistical Mechanics and its Applications, vol. 506, pp. 661–670, 2018. https://doi.org/10.1016/j.physa.2018.04.084
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Kauai, HI: IEEE Computer Society, 2001, pp. 511–518. https://doi.org/10.1109/CVPR.2001.990517
J. Bins, L. L. Dihl, and C. R. Jung, “Target tracking using multiple patches and weighted vector median filters,” Journal of Mathematical Imaging and Vision, vol. 45, pp. 293–307, 2013. https://doi.org/10.1007/s10851-012-0354-y
E. T. Hall, The Hidden Dimension. Garden City, NY: Doubleday, 1966.
R. M. Favaretto, L. Dihl, S. R. Musse, F. Vilanova, and A. B. Costa, “Using big-five personality model to detect cultural aspects in crowds,” in Conference on Graphics, Patterns and Images (SIBGRAPI). Niteroi, RJ: IEEE, 2017, pp. 223–229. https://doi.org/10.1109/SIBGRAPI.2017.36
R. M. Favaretto, L. Dihl, and S. R. Musse, “Detecting crowd features in video sequences,” in Proceedings of Conference on Graphics, Patterns and Images (SIBGRAPI). So Jos dos Campos, SP: IEEE Computer Society, 2016, pp. 201–208. https://doi.org/10.1109/SIBGRAPI.2016.036
P. Costa and R. R. McCrae, NEO PI-R - Inventrio de Personalidade NEO Revisado. So Paulo, SP: Vetor, 2007.
R. M. Favaretto, P. Knob, S. R. Musse, F. Vilanova, and A. B. Costa, “Detecting personality and emotion traits in crowds from video sequences,” Machine Vision and Applications, vol. 30, no. 5, pp. 999–1012, Jul 2019. https://doi.org/10.1007/s00138-018-0979-y
V. Araujo, R. Migon Favaretto, P. Knob, S. Raupp Musse, F. Vilanova, and A. Brandelli Costa, “How much do you perceive this?: An analysis on perceptions of geometric features, personalities and emotions in virtual humans,” in Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents. New York, NY, USA: ACM, 2019, pp. 179–181. https://doi.org/10.1101/622167
G. Hofstede, G. J. Hofstede, and M. Minkov, Cultures and organizations: Software of the mind. New York, NY: McGraw-Hill, 1991.
R. M. Favaretto, L. Dihl, R. Barreto, and S. R. Musse, “Using group behaviors to detect hofstede cultural dimensions,” in International Conference on Image Processing (ICIP). Phoenix, AZ: IEEE, 2016, pp. 2936–2940. https://doi.org/10.1109/ICIP.2016.7532897
F. Vilanova, F. M. Beria., A. B. Costa, and S. H. Koller, “Deindividua-tion: From le bon to the social identity model of deindividuation effects,” Cogent Psychology, vol. 4, pp. 1–21, 2017. https://doi.org/10.1080/23311908.2017.1308104
G. L. Bon, A Psicologia das Multides. Paris, FR: Presses Universitaires de France, 1986.
S. R. M. Rodolfo M. Favaretto and A. B. Costa, Emotion, Personality and Cultural Aspects in Crowds: Towards a Geometrical Mind. London, UK: Springer Nature, 2019. https://doi.org/10.1007/978-3-030-22078-5
R. Migon Favaretto, R. Rosa dos Santos, S. Raupp Musse, F. Vilanova, and A. Brandelli Costa, “Investigating cultural aspects in the fundamental diagram using convolutional neural networks and virtual agent simulation,” Computer Animation and Virtual Worlds, vol. 30, no. 3-4, p. e1899, 2019, e1899 cav.1899. https://doi.org/10.1002/cav.1899
P. Knob, M. Alcntara, E. Testa, R. Favaretto, G. Lima, L. Dihl, and S. R. Musse, “Generating background npcs motion and grouping behavior based on real video sequences,” Entertainment Computing, vol. 27, pp. 179–187, 2018. https://doi.org/10.1016/j.entcom.2018.06.003
V. Araujo, R. Migon Favaretto, P. Knob, S. Raupp Musse, F. Vilanova, and A. Brandelli Costa, “How much do you perceive this?: An analysis on perceptions of geometric features, personalities and emotions in virtual humans,” in Proceedings of the 19th ACMInternational Conference on Intelligent Virtual Agents, ser. IVA ’19. New York, NY, USA: ACM, 2019, pp. 179–181. https://doi.org/10.1145/3308532.3329454
M. Alcantara, E. Testa, G. L. da Silva, R. Favaretto, L. Dihl, and S. Musse, “Generating background population for games based on real video sequences,” in Brazilian Symposium on Games and Digital Entertainment (SBGames). So Paulo, SP: SBGames, 2016, pp. 66–72.
L. Dihl, E. Testa, P. Knob, G. Lima, R. M. Favaretto, M. Alcantara, and S. R. Musse, “Generating cultural characters based on hofstede dimensions,” in Virtual Humans and Crowds for Immersive Environments (VHCIE). Los Angeles, CA: IEEE, 2017, pp. 1–6. https://doi.org/10.1109/VHCIE.2017.7935621
P. Knob, V. F. de A. Araujo, R. M. Favaretto, and S. R. Musse, “Visualization of interactions in crowd simulation and video sequences,” in XVII Brazilian Symposium on Games and Digital Entertainment (SBGames). Foz do Iguau, PR: SBGames, 2018, pp. 601–610. https://doi.org/10.1109/SBGAMES.2018.00037
R. M. Favaretto, “Geomind: a software to extract emotion, personality and cultural aspects from video sequences.” 2019. [Online]. Available: https://www.rmfavaretto.pro.br/geomind
R. M. Favaretto, “Cultural crowds dataset: a set of videos from several countries,” 2018. [Online]. Available: https://www.rmfavaretto.pro.br/vhlab/datasets.php
Publicado
28/10/2019
Como Citar
FAVARETTO, Rodolfo Migon; MUSSE, Soraia Raupp.
Emotion, Personality and Cultural Aspects in Crowds: towards a Geometrical Mind. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 98-104.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8308.
