Real-time Crowd Research Articles

Real-Time Crowd Simulation Integrating Potential Fields and Agent Method

Crowd simulation is studied extensively in computer graphics, animation, and safety. A real-time crowd simulator has been developed based on potential fields and agent approach in this article. This simulator produces realistic complex heterogeneous motion and improves the simulation rates by at least 32% in comparison with the potential field results. The model of this simulator can efficiently tackle the problems in global optimal navigation, collision avoidance, and dynamic interaction; furthermore, it allows an agent to make independent decisions.

CrowdAR: Augmenting Live Video with a Real-Time Crowd

Finding and tracking targets and events in a live video feed is important for many commercial applications, from CCTV surveillance used by police and security firms, to the rapid mapping of events from aerial imagery. However, descriptions of targets are typically provided in natural language by the end users, and interpreting these in the context of a live video stream is a complex task. Due to current limitations in artificial intelligence, especially vision, this task cannot be automated and instead requires human supervision. Hence, in this paper, we consider the use of real-time crowdsourcing to identify and track targets given by a natural language description. In particular we present a novel method for augmenting live video with a real-time crowd.

Feature Cluster on “Evolutionary multiobjective optimization”

In the current digital era, at many places crowd counting mechanisms still rely on old-fashioned methods such as maintaining registers, making use of people counters and sensors based counting at entrance. These methods fail in the places where the movement of people is completely random, highly variable and dynamic. These methods are time consuming and tedious. The proposed system is developed for situations where emergency evacuations are required such as fire outbreaks, calamitous events, etc. and making informed decisions on the basis of the number of people such as food, water, detecting congestion, etc. A deep convolution neural network (DCNN) based system can be used for near real-time crowd counting. The system uses NVIDIA GPU processor to exploit the parallel computing framework to achieve swift and agile processing of the video feed taken through a camera. This work contributes towards constructing a model to detect heads captured by CCTV camera. The model is trained extensively by providing several scenarios such as overlapping heads, partial visibility of heads etc. This system provides significant accuracy in estimating the head count in dense population in reasonably less amount of time.

An Improved Potential Field Based Method for Crowd Simulation

Crowd simulation explores crowd behavior in virtual environments, which has been extensively studied in many areas, such as safety and civil engineering, transportation, social science, and entertainment industry. In this paper, an improved potential field method is proposed to achieve the real-time crowd simulation, which is composed of the global navigation with Dijkstra's algorithm and the potential field based local navigation. First, a region separation is performed to divide the environment into a set of triangles, and thus a topological graph can be built with the triangles as vertices. Then a velocity-density model is introduced for improving the speed controlling mechanism and solving the "maximum speed dilemma" which means the velocity of an individual derived by potential field will be stuck into the maximum due to the ill speed control. Since the movement of an individual in the crowd is influenced by the socio-psychological forces, the individuals' actions express the group attributes. In order to represent the group attributes in the crowd, the repulsive potential function is improved in this paper. Experiments have been carried out and the results show that the improved potential field based method can simulate the crowd in real time and avoid the "maximum speed dilemma".

이미지 기반의 유도장과 항해장을 활용한 실시간 대규모 군중 시뮬레이션

In large-scale crowd simulations, it is very important for the decision-making system of manipulating interactive behaviors to minimize the computational cost for controlling realistic behaviors such as collision avoidance. In this paper, we propose a large-scale realtime crowd simulation method using the affordance and navigation potential fields such as attractive and repulsive forces of electromagnetic fields. In particular, the model that we propose locally handles the realistic interactions between agents, and thus radically reduces the cost of expensive computation on interactions which has been the most problematic in crowd simulation. Our method is widely applicable to the expression and analysis of various crowd behaviors that are needed in behavior control in computer games, crowd scenes in movies, emergent behaviors of evacuation, etc.

Deformable polygonal agents in crowd simulation

ABSTRACTTo produce impressive virtual worlds, real‐time crowd simulations require large and detailed scenes populated by agents with complex shapes and geometry. For efficiency reasons, these agents are usually approximated by point‐like representations to optimize the performances of collision avoidance and interactions between agents. This paper addresses the issue of handling deformable polygonal agents with arbitrary shapes in real time crowd simulations. The proposed multiresolution framework supports environments with arbitrary topologies and provides tools for efficient proximity queries. Copyright © 2014 John Wiley & Sons, Ltd.

Comparison of Field-based Crowd Simulation Approach Based on Different Parallel Architectures

Crowd simulation has been very significant and widely applied in many fields of virtual reality. How to greatly increase the efficiency and realism becomes the focus of the research about crowd simulation. In this paper, a real-time crowd simulation system is designed on parallel architectures. The system is applied to simulating the evacuation of massive crowd in an Olympic venue and then experiments are done to compare and analyze their different simulation efficiencies on different architectures. The best simulation performance comes from the hybrid architecture in combination of CPU and GPU with parallel computing on multi-core CPUs and the it can reach 52 fps when the number of crowd is 10000. GPU architecture can be used in the crowd simulation system with few data transmission times between CPU and GPU, branch statements and texture look-ups better.

Scene-adaptive accurate and fast vertical crowd counting via joint using depth and color information

Reliable and real-time crowd counting is one of the most important tasks in intelligent visual surveillance systems. Most previous works only count passing people based on color information. Owing to the restrictions of color information influences themselves for multimedia processing, they will be affected inevitably by the unpredictable complex environments (e.g. illumination, occlusion, and shadow). To overcome this bottleneck, we propose a new algorithm by multimodal joint information processing for crowd counting. In our method, we use color and depth information together with a ordinary depth camera (e.g. Microsoft Kinect). Specifically, we first detect each head of the passing or still person in the surveillance region with adaptive modulation ability to varying scenes on depth information. Then, we track and count each detected head on color information. The characteristic advantage of our algorithm is that it is scene adaptive, which means the algorithm can be applied into all kinds of different scenes directly without additional conditions. Based on the proposed approach, we have built a practical system for robust and fast crowd counting facing complicated scenes. Extensive experimental results show the effectiveness of our proposed method.

A GPU-assisted hybrid model for real-time crowd simulations

In this paper, we propose two new techniques for real-time crowd simulations; the first one is the clustering of agents on the GPU and the second one is incorporating the global cluster information into the existing microscopic navigation technique. The proposed model combines the agent-based models with macroscopic information (agent clusters) into a single framework. The global cluster information is determined on the GPU, and based on the agents' positions and velocities. Then, this information is used as input for the existing agent-based models (velocity obstacles, rule-based steering and social forces). The proposed hybrid model not only considers the nearby agents but also the distant agent configurations. Our test scenarios indicate that, in very dense circumstances, agents that use the proposed hybrid model navigate the environment with actual speeds closer to their intended speeds (less stuck) than the agents that are using only the agent-based models.

Real-time crowd density mapping using a novel sensory fusion model of infrared and visual systems

Crowd dynamic management research has seen significant attention in recent years in research and industry in an attempt to improve safety level and management of large scale events and in large public places such as stadiums, theatres, railway stations, subways and other places where high flow of people at high densities is expected. Failure to detect the crowd behaviour at the right time could lead to unnecessary injuries and fatalities. Over the past decades there have been many incidents of crowd which caused major injuries and fatalities and lead to physical damages. Examples of crowd disasters occurred in past decades include the tragedy of Hillsborough football stadium at Sheffield where at least 93 football supporters have been killed and 400 injured in 1989 in Britain’s worst-ever sporting disaster (BBC, 1989). Recently in Cambodia a pedestrians stampede during the Water Festival celebration resulted in 345 deaths and 400 injuries (BBC, 2010) and in 2011 at least 16 people were killed and 50 others were injured in a stampede in the northern Indian town of Haridwar (BBC, 2011). Such disasters could be avoided or losses reduced by using different technologies. Crowd simulation models have been found effective in the prediction of potential crowd hazards in critical situations and thus help in reducing fatalities. However, there is a need to combine the advancement in simulation with real time crowd characterisation such as the estimation of real time density in order to provide accurate prognosis in crowd behaviour and enhance crowd management and safety, particularly in mega event such as the Hajj. This paper addresses the use of novel sensory technology in order to estimate people’s dynamic density during one of the Hajj activities. The ultimate goal is that real time accurate estimation of density in different areas within the crowd could help to improve the decision making process and provide more accurate prediction of the crowd dynamics. This paper investigates the use of infrared and visual cameras supported by auxiliary sensors and artificial intelligence to evaluate the accuracy in estimating crowd density in an open space during Muslims Pilgrimage to Makkah (Mecca).

Context aware ad hoc network for mitigation of crowd disasters

Our research works focuses on the design and implementation of a novel ubiquitous multi context-aware mobile phone sensing network for mitigation of crowd disasters using machine-to-machine (M2M) communications. A mobile sensor network system integrated with wireless multimedia sensor networks (WMSNs) was designed for effective prediction of a stampede during crowd disasters. This proposed sensor network consists of mobile devices that are used as crowd monitoring participant nodes that employ light sensors, accelerometers, as well as audio and video sensors to collect the relevant data. Real-time crowd dynamics modeling and real-time activity modeling have been achieved by implementing the algorithms developed for Context Acquisition and multi-context fusion. Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus. The implementation of the proposed architecture in Android smartphone provides light-weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation and generation of an in time alert to take measures to avoid the occurrence of a stampede. The system has been tested and illustrated within a group of people for stampede prediction by using empirically collected data.

Stream-based animation of real-time crowd scenes

We present a new way of drawing a crowd of animated characters in real-time. Previous work has focused almost exclusively on how to visualize ever larger crowd scenes and the current state-of-the-art can display tens of thousands of virtual humans with ease. The associated trade-off, however, is that crowd members can do little more than play a set of scripted motion clips. It follows that designating individuals to be members of a crowd instantly limits the techniques that can be used, the behaviours that can be depicted and ultimately, the perceived realism of a scene. Our approach differs from the state-of-the-art in that we do not propose a crowd-specific technique but instead a bone-parallel, OpenCL-accelerated interpretation of the traditional character pipeline. The method does not rely on pre-processing; provides fine-grained control over the animation of a crowd (support for motion blending and varied skeletons, for example) and crowd members and user-controlled ‘hero’ characters can be handled without distinction.

Editorial for the CAVW special issue on real-time crowd simulation

Computer Animation and Virtual WorldsVolume 23, Issue 1 p. 1-1 EDITORIAL Editorial for the CAVW special issue on real-time crowd simulation Daniel Thalmann, Daniel ThalmannSearch for more papers by this authorSaad Ali, Saad AliSearch for more papers by this authorPetros Faloutsos, Petros FaloutsosSearch for more papers by this author Daniel Thalmann, Daniel ThalmannSearch for more papers by this authorSaad Ali, Saad AliSearch for more papers by this authorPetros Faloutsos, Petros FaloutsosSearch for more papers by this author First published: 21 February 2012 https://doi.org/10.1002/cav.1425Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume23, Issue1January/February 2012Pages 1-1 RelatedInformation

YaQ: An Architecture for Real-Time Navigation and Rendering of Varied Crowds

The YaQ software platform is a complete system dedicated to real-time crowd simulation and rendering. Fitting multiple application domains, such as video games and VR, YaQ aims to provide efficient algorithms to generate crowds comprising up to thousands of varied virtual humans navigating in large-scale, global environments.

Automatic spatial analysis and pedestrian flow control for real-time crowd simulation in an urban environment

Distributing a collection of virtual humans throughout a large urban environment, where limited semantic information is available, poses a problem when attempting to create a visually realistic real-time environment. Randomly positioning agents within an urban environment will not cover the environment with virtual humans in a plausible way. For example, areas of the urban space that are more frequently used should have a higher population density both at the start and during the simulation. It is infeasible to manually identify all the areas in the urban environment which should be crowded or sparsely populated when considering a scalable method, suitable for large environments. Consequently, this paper combines and extends techniques from spatial analysis and virtual agent behaviour simulations to develop a system capable of automatically distributing pedestrians in an urban environment. In particular, it extends the Point-Based Space Syntax technique to enable the automatic analysis of a large urban environment in the presence of limited contextual information. This analysis specifies a set of population densities for areas in the environment and these values are used to initialise the locations of all the virtual humans in the environment. In addition to the initialisation stage the population densities in each area are consulted to ensure that the correct distribution of virtual humans is maintained throughout the simulation. The system is tested on an arbitrary section of a real city and comparisons of the characteristic parts of the test environment are correlated with the pedestrian movements.

Special issue on Cyberworlds’2007

The sixth International Conference on Cyberworlds (Cyberworlds 2007) took place in the beautiful town of Hannover, Germany, in October 2007. The HAPTEX Workshop 2007 was co-hosted with the conference. The first workshop on Cyberworlds was held as the International Workshop on Synthetic Worlds in the University of Aizu, Japan, in 1993, to explore the meaning, philosophy, and the potential of the worlds synthesized on the web as well as in computational spaces, in general. Since then, the conferences are organized yearly. The HAPTEX workshop is the second workshop held under the project “HAPtic sensing of virtual TEXtiles” (HAPTEX). The project is funded under the Sixth Framework Programme (FP6) of the European Union and is coordinated by Prof. Nadia Magnenat-Thalmann who heads MIRALab at the University of Geneva, Switzerland. Five best papers from the Cyberworlds conference and two best papers from the HAPTEX workshop have been selected for this special issue of The Visual Computer. The papers have been extended and carefully revised for the journal. The papers reflect some of the diverse subject areas included under the banner of cyberworlds and haptic interaction. We start with “Real-Time Crowd Motion Planning: Scalable Avoidance and Group Behavior” which deals with the difficult task of real-time motion planning for large

Continuum crowds

We present a real-time crowd model based on continuum dynamics. In our model, a dynamic potential field simultaneously integrates global navigation with moving obstacles such as other people, efficiently solving for the motion of large crowds without the need for explicit collision avoidance. Simulations created with our system run at interactive rates, demonstrate smooth flow under a variety of conditions, and naturally exhibit emergent phenomena that have been observed in real crowds.

Understanding and planning for different spectator crowds: A. E. Berlonghi, Safety Science, 18(4), 239–247

Crowd dynamic management research has seen significant attention in recent years in research and industry in an attempt to improve safety level and management of large scale events and in large public places such as stadiums, theatres, railway stations, subways and other places where high flow of people at high densities is expected. Failure to detect the crowd behaviour at the right time could lead to unnecessary injuries and fatalities. Over the past decades there have been many incidents of crowd which caused major injuries and fatalities and lead to physical damages. Examples of crowd disasters occurred in past decades include the tragedy of Hillsborough football stadium at Sheffield where at least 93 football supporters have been killed and 400 injured in 1989 in Britain’s worst-ever sporting disaster (BBC, 1989). Recently in Cambodia a pedestrians stampede during the Water Festival celebration resulted in 345 deaths and 400 injuries (BBC, 2010) and in 2011 at least 16 people were killed and 50 others were injured in a stampede in the northern Indian town of Haridwar (BBC, 2011). Such disasters could be avoided or losses reduced by using different technologies. Crowd simulation models have been found effective in the prediction of potential crowd hazards in critical situations and thus help in reducing fatalities. However, there is a need to combine the advancement in simulation with real time crowd characterisation such as the estimation of real time density in order to provide accurate prognosis in crowd behaviour and enhance crowd management and safety, particularly in mega event such as the Hajj. This paper addresses the use of novel sensory technology in order to estimate people’s dynamic density during one of the Hajj activities. The ultimate goal is that real time accurate estimation of density in different areas within the crowd could help to improve the decision making process and provide more accurate prediction of the crowd dynamics. This paper investigates the use of infrared and visual cameras supported by auxiliary sensors and artificial intelligence to evaluate the accuracy in estimating crowd density in an open space during Muslims Pilgrimage to Makkah (Mecca).