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IEEE Transactions on Pattern Analysis and Machine Intelligence
Group Behaviors for Systems with Significant Dynamics
Autonomous Robots
Intuitive Crowd Behaviour in Dense Urban Environments using Local Laws
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Computational Geometry: Theory and Applications
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SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
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Proceedings of the 2007 ACM symposium on Virtual reality software and technology
Interactive navigation of multiple agents in crowded environments
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Software debugging, testing, and verification
IBM Systems Journal
Robust classification of strokes with SVM and grouping
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
An Open Framework for Developing, Evaluating, and Sharing Steering Algorithms
MIG '09 Proceedings of the 2nd International Workshop on Motion in Games
Scenario space: characterizing coverage, quality, and failure of steering algorithms
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Imperceptible relaxation of collision avoidance constraints in virtual crowds
Proceedings of the 2011 SIGGRAPH Asia Conference
Improved benchmarking for steering algorithms
MIG'11 Proceedings of the 4th international conference on Motion in Games
Towards a quantitative approach for comparing crowds
Computer Animation and Virtual Worlds
Toolkit for teaching steering behaviors for 3D human-like virtual agents (demonstration)
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EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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The size of crowds that modern computer games and urban simulations are capable of handling has given rise to the challenging problem of debugging and testing massive simulations of autonomous agents. In this paper, we propose SteerBug: an interactive framework for specifying and detecting steering behaviors. Our framework computes a set of time-varying metrics for agents and their environment, which characterize steering behaviors. We identify behaviors of interest by applying conditions (rules) or user defined sketches on the associated metrics. The behaviors we can specify and detect include unnatural steering, plainly incorrect results, or application-specific behaviors of interest. Our framework is extensible and independent of the specifics of any steering approach. To our knowledge, this is the first work that aims to provide a computational framework for specifying and detecting crowd behaviors in animation.