Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Group behavior from video: a data-driven approach to crowd simulation
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Experiment-based modeling, simulation and validation of interactions between virtual walkers
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
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In this paper, we propose a data-driven crowd behavior model that is constructed by extracting examples from human motion data describing how humans make decisions. We cluster the examples before the simulation to find similar patterns of behavior. During the simulation, at each simulation time step, we first classify the input state perceived by an agent in the simulation into one example cluster using an artificial neural network classifier. We then combine similar examples of that cluster to produce an output, a velocity vector indicating the position of the agent in the next time step. Such a two step matching process enables the selection of the most similar example accurately and efficiently. To verify our approach, we have developed an initial prototype in which we build our model using motion data generated by a RVO2 simulator, attempting to reproduce the behavior of the RVO2 model. By comparing the position of the same agent simulated by the RVO2 mode land our model respectively at the same time steps, we show that our model has the ability to reproduce the behavior of the RVO2model accurately. As future work, we will use real human motion data as model input, so that our model may perform human-like motion behavior.