A hierarchical Bayesian model for crowd emotions
| Autor: | Mirza Sulman Baig, Oscar J. Urizar, Carlo S. Regazzoni, Matthias Rauterberg, Lucio Marcenaro, Emilia I. Barakova |
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| Přispěvatelé: | Industrial Design |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Neuroscience (miscellaneous) Poison control crowd behavior emotion estimation in crowds estimation of individual and collective emotions 02 engineering and technology Bayesian inference Computer security computer.software_genre Machine learning 050105 experimental psychology Motion (physics) Cellular and Molecular Neuroscience Crowds 0202 electrical engineering electronic engineering information engineering Crowd behavior 0501 psychology and cognitive sciences Crowd psychology Original Research business.industry 05 social sciences Probabilistic logic Intelligent decision support system Bayesian network Estimation of individual and collective emotions 020201 artificial intelligence & image processing Artificial intelligence business computer Neuroscience Emotion estimation in crowds |
| Zdroj: | Frontiers in Computational Neuroscience Frontiers in Computational Neuroscience, 10:63. Frontiers Research Foundation |
| ISSN: | 1662-5188 |
| Popis: | Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the behavior of individuals and of the crowd as a single entity, and explore the relation between behavior and emotions to infer emotional states. Information about the motion patterns of individuals are described using a self-organizing map, and a hierarchical Bayesian network builds probabilistic models to identify behaviors and infer the emotional state of individuals and the crowd. This model is trained and tested using data produced from simulated scenarios that resemble real-life environments. The conducted experiments tested the efficiency of our method to learn, detect and associate behaviors with emotional states yielding accuracy levels of 74% for individuals and 81% for the crowd, similar in performance with existing methods for pedestrian behavior detection but with novel concepts regarding the analysis of crowds. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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