Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Artificial fishes: physics, locomotion, perception, behavior
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Hierarchical Model for Real Time Simulation of Virtual Human Crowds
IEEE Transactions on Visualization and Computer Graphics
Trajectory Segmentation Using Dynamic Programming
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Simulating virtual crowds in emergency situations
Proceedings of the ACM symposium on Virtual reality software and technology
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
International Journal of Computer Vision
ACM SIGGRAPH 2006 Papers
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
Experiment-based modeling, simulation and validation of interactions between virtual walkers
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A synthetic-vision based steering approach for crowd simulation
ACM SIGGRAPH 2010 papers
ACM SIGGRAPH Asia 2010 papers
Improving data association by joint modeling of pedestrian trajectories and groupings
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Simulating the local behaviour of small pedestrian groups
Proceedings of the 17th ACM Symposium on Virtual Reality Software and Technology
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We present a crowd simulation that captures some of the semantics of a specific scene by partly reproducing its motion behaviors, both at a lower level using a steering model and at the higher level of goal selection. To this end, we use and generalize a steering model based on linear velocity prediction, termed LTA. From a goal selection perspective, we reproduce many of the motion behaviors of the scene without explicitly specifying them. Behaviors like "wait at the tram stop" or "stroll-around" are not explicitly modeled, but learned from real examples. To this end, we process real data to extract information that we use in our simulation. As a consequence, we can easily integrate real and virtual agents in a mixed reality simulation. We propose two strategies to achieve this goal and validate the results by a user study.