Measures for the organization of self-organizing maps
Self-Organizing neural networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Supervised Image Classification by SOM Activity Map Comparison
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Motion estimation with edge continuity constraint for crowd scene analysis
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Mining paths of complex crowd scenes
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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This paper introduces the use of self-organizing maps for the visualization of crowd dynamics and to learn models of the dominant motions of crowds in complex scenes. The self-organizing map (SOM) model is a well known dimensionality reduction method proved to bear resemblance with characteristics of the human brain, representing sensory input by topologically ordered computational maps. This paper proposes algorithms to learn and compare crowd dynamics with the SOM model. Different information is employed as input to the used SOM. Qualitative and quantitative results are presented in the paper.