Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
ACM SIGGRAPH 2006 Papers
Efficient K-Means Clustering Using Accelerated Graphics Processors
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation)
Activity-Driven Populace: A Cognitive Approach to Crowd Simulation
IEEE Computer Graphics and Applications
YaQ: An Architecture for Real-Time Navigation and Rendering of Varied Crowds
IEEE Computer Graphics and Applications
Aggregate dynamics for dense crowd simulation
ACM SIGGRAPH Asia 2009 papers
Challenges in Crowd Simulation
CW '09 Proceedings of the 2009 International Conference on CyberWorlds
A Neighborhood Grid Data Structure for Massive 3D Crowd Simulation on GPU
SBGAMES '09 Proceedings of the 2009 VIII Brazilian Symposium on Games and Digital Entertainment
PLEdestrians: a least-effort approach to crowd simulation
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Scalable clustering using graphics processors
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Hybrid long-range collision avoidance for crowd simulation
Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
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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.