A Conceptual Framework for Modelling Crowd Behaviour

Barbosa, Jorge Luís Victória; Hahn, Roberto Carlos; Bonatto, Daniel Torres; Cecin, F.R.; Geyer, Cláudio F. R.

doi:10.1109/ds-rt.2007.26

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…The model proposed in this paper exploits some of the principles underlying the OCC model but embeds them in a neurological context that includes theories that cover other aspects as well, thus providing a deeper and wider level of understanding of social decision making. In VICTEC architecture for modelling crowd behaviour [39] the OCC framework was also used for modelling emotions. However, in contrast to our model, VICTEC does not embed the OCC framework in a broader context of social decision making based on neurological principles.…”

Section: Related Workmentioning

confidence: 99%

Evacuation Simulation Based on Cognitive Decision Making Model in a Socio-Technical System

Zia

Riener

Ferscha

et al. 2011

2011 IEEE/ACM 15th International Symposium on Distributed Simulation and Real Time Applications

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In the last decade the shift of information and communication systems from a purely system level to the social level is already observable. However, the integration of social / cognitive aspects into so-called social computing is not an easy task due to conceptual differences in domains. A SocioTechnical System (STS) is a recent term intending to differentiate between a social system mediated by natural sciences or information technology. Even if the mediation of social / cognitive aspects is "theoretically" governed by technology, the gap between "socio" and "technical" is historical and huge. Furthermore the fact that with every passing year, the technical systems become more intelligent with respect to interaction with people and their pervasiveness, a special attention should be given to modelling both social and technical components and interaction between them. For example while modelling (and simulating) an emergency situation from a public facility, the possible availability of technology at the environment (e.g. situation-aware exit signs, interactive displays, etc.) and personal (e.g. cell phones, specialized wearables etc.) level, along with its social / cognitive influence must not be overruled. To address this challenge, we have integrated cognitive decision making model abstracted from psychological, neurological and social theories of human behaviour during evacuation situations into CA based simulation. Keeping focus on a scenario in which a small population of agents is technologically assisted, some of the most interesting finding are: (i) the inclusion of a representative and authentic social behaviour model into modelling a socio-technical system essentially produces fundamental differences in methodologies, (ii) the technologically assisted agents emerge as leaders during evacuation changing the intentions of many agents within their influence (iii) even a small population of such leaders in sufficiently large population is enough to guarantee a remarkable difference; particularly improving usage of possibly under-utilized exits.

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Section: Related Workmentioning

confidence: 99%

Evacuation Simulation Based on Cognitive Decision Making Model in a Socio-Technical System

Zia

Riener

Ferscha

et al. 2011

2011 IEEE/ACM 15th International Symposium on Distributed Simulation and Real Time Applications

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“…A primary concern in surveillance and monitoring systems is to identify human crowd behaviors and supervise the crowd to prevent disasters and unforeseen events [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. The analysis of human behavior in crowded scenes is one of the most important and challenging areas in current research [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Traditional visual surveillance systems that depend purely on manpower to analyze videos is inefficient because of the enormous number of cameras and screens that require monitoring, human fatigue due to time spent on lengthy monitoring periods, paucity of essential fore-knowledge and training in what to look for, and also because of the colossal amount of video data that is generated per day.…”

Section: Introductionmentioning

confidence: 99%

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Abdullah

Ghadi

Gochoo

et al. 2021

Entropy

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To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

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“…There is a different situation with the research that focuses on emotions as a social information source. There are only some studies that examine the affective interactions; these studies are sparse and distributed in a variety of domains: crowd modelling (Dey & Roberts, 2007), agent life extension in a multi-agent environment (Kazemifard et al, 2012), and training scenarios (Korecko et al, 2014). The lack of such research is related to the social effects of emotions being a comparatively new direction in psychology and sociology.…”

Section: Introductionmentioning

confidence: 99%

Agent-based Human Group Affective State Modelling

Pudāne

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Affective states influence how humans make decisions, what actions they choose to perform, and how group structure and relationships among individuals develop over time. The studies on emotion modelling in computer systems have developed in number and quality over the past 30 years. However, there are no models that allow to fully simulate human affective interaction. The study in the Thesis integrates existing models and interaction mechanisms found in psychology research, using multi-agent systems to implement affective interaction mechanisms.

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A Conceptual Framework for Modelling Crowd Behaviour

Cited by 8 publications

References 5 publications

Evacuation Simulation Based on Cognitive Decision Making Model in a Socio-Technical System

Evacuation Simulation Based on Cognitive Decision Making Model in a Socio-Technical System

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Agent-based Human Group Affective State Modelling

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