On the modelling of crowd dynamics by generalized kinetic models

Dogbe, Christian

doi:10.1016/j.jmaa.2011.09.007

Cited by 47 publications

(20 citation statements)

References 34 publications

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“…Although no psychological connection between the groups is modeled, affiliative theories are often referenced (e.g., Aveni, 1977 ) to justify the inclusion of a group which stays together in a crowd situation (e.g., Feinberg & Johnson, 1995 ). For example, Dogbe (2012) modeled group behavior using attraction and repulsion interactions, where social groups (assumed to be friends and family in this model) are attracted to move together throughout the simulation, but are repulsed by other neighboring groups. By implementing group behavior in this way, Dogbe is simulating a crowd where the groups are essentially small numbers of people clumped together within the crowd, with no meaningful interaction other than to change formation in order to stay together as they move throughout the crowd.…”

Section: Resultsmentioning

confidence: 99%

From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling

Templeton

Drury

Philippides

2015

Review of General Psychology

111

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Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature (N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory.

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Section: Resultsmentioning

confidence: 99%

From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling

Templeton

Drury

Philippides

2015

Review of General Psychology

111

View full text Add to dashboard Cite

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“…Non-Local Crowd Dynamics (NLCD) [26][27][28] with a discrete element method (DEM) is a variant of macroscopic models. In the NLCD, each individual moves toward a fixed target, deviating from the best path according to the crowd distribution to avoid entering regions with higher densities.…”

Section: Physics-mathematics Crowd Models (Pmcm)mentioning

confidence: 99%

Crowd Models for Emergency Evacuation: A Review Targeting Human-Centered Sensing

Radianti

Granmo

Bouhmala

et al. 2013

2013 46th Hawaii International Conference on System Sciences

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Emergency evacuation of crowds is a fascinating phenomenon that has attracted researchers from various fields. Better understanding of this class of crowd behavior opens up for improving evacuation policies and smarter design of buildings, increasing safety. Recently, a new class of disruptive technology has appeared: Humancentered sensing which allows crowd behavior to be monitored in real-time, and provides the basis for realtime crowd control. The question then becomes: to what degree can previous crowd models incorporate this development, and what areas need further research? In this paper, we provide a survey that describes some widely used crowd models and discuss their advantages and shortages from the angle of human-centered sensing. Our review reveals important research opportunities that may contribute to an improved and more robust emergency management.

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“…The relative size of people will be even smaller and the camera configuration means that often only the head/shoulders is consistently visable. Future plans are to apply this work to real data from project partners, to use research datasets such as CUHK occlusion 1 to improve the ability to track occluded persons and to examine applying fluid dynamics models (such as [8]) to recognise other crowd-based behaviour and events.…”

Section: Person Trackingmentioning

confidence: 99%

An information retrieval approach to identifying infrequent events in surveillance video

Little

Jargalsaikhan

Clawson

et al. 2013

Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval

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This paper presents work on integrating multiple computer visionbased approaches to surveillance video analysis to support user retrieval of video segments showing human activities. Applied computer vision using real-world surveillance video data is an extremely challenging research problem, independently of any information retrieval (IR) issues. Here we describe the issues faced in developing both generic and specific analysis tools and how they were integrated for use in the new TRECVid interactive surveillance event detection task. We present an interaction paradigm and discuss the outcomes from face-to-face end user trials and the resulting feedback on the system from both professionals, who manage surveillance video, and computer vision or machine learning experts. We propose an information retrieval approach to finding events in surveillance video rather than solely relying on traditional annotation using specifically trained classifiers. Categories and Subject Descriptors VIDEO SURVEILLANCE EVENT DETECTIONEfficiently and reliably finding complex events of interest in video surveillance footage presents several challenges. These include: the volume of data to be processed, especially in relation to the frequency of event occurrence; the low resolution and high noise of the video including activity occlusion and multiple co-occurring * Contact author: suzanne.little@dcu.ie Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$15.00. events; the low inter-class variance between many events and, finally, users' misleading expectations of the computer's ability to accurately and confidently identify people, objects and activities. In this paper we present the outcomes from our experiences in applying multiple computer-vision based approaches to support interactive user retrieval of video segments from surveillance video with feedback from end users including professionals from major companies in transport and event stadium surveillance and security.By surveillance video we are generally considering fixed CCTV cameras placed for security observation purposes where video archives will be accessed post-event for investigative or judicial reasons. Events of interest may include (but are not limited to): unauthorised access, accident, anti-social behaviour, abandoned luggage, unauthorised photography/filming, sabotage or equipment tampering, etc. Such complex events can be identified with varying but generally low levels of success using computer vision techniques. Simpler analysis tasks include person detection, gesture modelling and object detection that can then be used by semantic classifiers for the more compl...

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On the modelling of crowd dynamics by generalized kinetic models

Cited by 47 publications

References 34 publications

From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling

From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling

Crowd Models for Emergency Evacuation: A Review Targeting Human-Centered Sensing

An information retrieval approach to identifying infrequent events in surveillance video

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