Simulating Human Activities for Synthetic Inputs to Sensor Systems

We are developing human activity simulations that could be used to test distributed video sensor networks. Our ultimate goals are to build statistical models of pedestrian density and flows at a number of urban locations and to correlate those flows with population movement and density models represented in a spatiotemporal modeling system. In order to create known populace flows, we have built a virtual populace simulation system, called CAROSA, which permits the authoring of functional crowds of people going about role-, context-, and schedule-dependent activities. The capabilities and authoring tools for these functional crowd simulations are described with the intention of readily creating ground truth data for distributed sensor system design and evaluation.

[1]  Demetri Terzopoulos,et al.  Autonomous pedestrians , 2005, SCA '05.

[2]  Steve Rabin,et al.  AI Game Programming Wisdom , 2002 .

[3]  Carol O'Sullivan,et al.  Eye-catching crowds: saliency based selective variation , 2009, ACM Trans. Graph..

[4]  Stephen Chenney,et al.  Flow tiles , 2004, SCA '04.

[5]  Daniel Thalmann,et al.  Direct 3D interaction with smart objects , 1999, VRST '99.

[6]  Norman I. Badler,et al.  ACUMEN: amplifying control and understanding of multiple entities , 2002, AAMAS '02.

[7]  Dimitris N. Metaxas,et al.  Eurographics/ Acm Siggraph Symposium on Computer Animation (2007) Group Behavior from Video: a Data-driven Approach to Crowd Simulation , 2022 .

[8]  Norman I. Badler,et al.  Modeling Crowd and Trained Leader Behavior during Building Evacuation , 2006, IEEE Computer Graphics and Applications.

[9]  Renata Vieira,et al.  Ontology-based crowd simulation for normal life situations , 2005, International 2005 Computer Graphics.

[10]  W ReynoldsCraig.  Flocks, herds and schools: A distributed behavioral model , 1987 .

[11]  Norman I. Badler,et al.  Building parameterized action representations from observation , 2000 .

[12]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[13]  D. Thalmann,et al.  Virtual Humans’ Behaviour: Individuals, Groups, and Crowds , 2000 .

[14]  Katherine Isbister,et al.  Social Implications of Using Synthetic Characters: An Examination of a Role-Specific Intelligent Agent , 1998 .

[15]  Taku Komura,et al.  Computing inverse kinematics with linear programming , 2005, VRST '05.

[16]  Yunqian Ma,et al.  Motion segmentation and activity representation in crowds , 2009, Int. J. Imaging Syst. Technol..

[17]  Norman I. Badler,et al.  Controlling individual agents in high-density crowd simulation , 2007, SCA '07.

[18]  David C. Brogan,et al.  Group Behaviors for Systems with Significant Dynamics , 1997, Auton. Robots.

[19]  Michael Gleicher,et al.  Scalable behaviors for crowd simulation , 2004, Comput. Graph. Forum.

[20]  Ashraf A. Kassim,et al.  Tracking a Variable Number of Human Groups in Video Using Probability Hypothesis Density , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[21]  Mitsuru Ishizuka,et al.  Social role awareness in animated agents , 2001, AGENTS '01.

[22]  Norman I. Badler,et al.  Virtual Crowds: Methods, Simulation, and Control , 2008, Virtual Crowds: Methods, Simulation, and Control.

[23]  Dinesh Manocha,et al.  Composite agents , 2008, SCA '08.

[24]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[25]  Demetri Terzopoulos,et al.  A decision network framework for the behavioral animation of virtual humans , 2007, SCA '07.

[26]  Justine Cassell,et al.  Crowd and Group Simulation with Levels of Detail for Geometry, Motion and Behaviour. , 2002 .

[27]  Daniel Cohen-Or,et al.  Fitting behaviors to pedestrian simulations , 2009, SCA '09.

[28]  Harry Gifford.  Crowd Simulation , 2013 .

[29]  Nigel Thrift,et al.  Times, Spaces and Places. A Chronogeographic Perspective , 1981 .