Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating motion graphs for character navigation
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
ACM SIGGRAPH 2006 Papers
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Crowd Simulation
Perceptual evaluation of position and orientation context rules for pedestrian formations
Proceedings of the 5th symposium on Applied perception in graphics and visualization
Real-time path planning for virtual agents in dynamic environments
ACM SIGGRAPH 2008 classes
Being a part of the crowd: towards validating VR crowds using presence
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Watch Out! A Framework for Evaluating Steering Behaviors
Motion in Games
Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation)
A statistical similarity measure for aggregate crowd dynamics
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Velocity-based modeling of physical interactions in multi-agent simulations
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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There are various techniques for simulating crowds, however, in most cases the quality of the simulation is measured by examining its "look-and-feel". Even if the aggregate movement of the crowd looks natural from afar, the behaviors of individuals might look odd when examined more closely. In this paper we present a data-driven approach for evaluating the behaviors of individuals within a simulated crowd. Each decision of an individual agent is expressed as a state-action pair, which stores a representation of the characteristics being evaluated and the factors that influence it. Based on video footage of a real crowd, a database of state-action examples is generated. Using a similarity measure, the queries are matched with the database of examples. The degree of similarity can be interpreted as the level of "naturalness" of the behavior. Essentially, this sort of evaluation offers an objective answer to the question of how similar are the simulated behaviors compared to real ones. By changing the input video we can change the context of evaluation.