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Group Behavior Recognition using Dynamic Bayesian Networks: Understanding intentions, goals and actions that take place inside teams - Softcover
Inhaltsangabe
In this PhD thesis we analyze the concepts involved in the decision making of groups of agents and apply these concepts in creating a framework for performing group behavior recognition. We present an overview of the intention theory, as studied by some great theorists such as Searle, Bratmann and Cohen, and show the link with more recent researches. We study the advantages and drawbacks of some techniques in the domain and create a new model for representing and detecting group behaviors, the aim being to create a unified approach of the problem. Most of this thesis is consecrated in the detailed presentation of the model as well as the algorithm responsible for behavior recognition. Our model is tested on two different applications involving human gesture analysis and multimodal fusion of audio and video data. By means of these applications, we advance the argument that multivariate sets of correlated data can be efficiently analyzed under a unified framework of behavior recognition. We show that the correlation between different sets of data can be modeled as cooperation inside a team and that behavior recognition is a modern approach of classification and pattern recognition.
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In this PhD thesis we analyze the concepts involved in the decision making of groups of agents and apply these concepts in creating a framework for performing group behavior recognition. We present an overview of the intention theory, as studied by some great theorists such as Searle, Bratmann and Cohen, and show the link with more recent researches. We study the advantages and drawbacks of some techniques in the domain and create a new model for representing and detecting group behaviors, the aim being to create a unified approach of the problem. Most of this thesis is consecrated in the detailed presentation of the model as well as the algorithm responsible for behavior recognition. Our model is tested on two different applications involving human gesture analysis and multimodal fusion of audio and video data. By means of these applications, we advance the argument that multivariate sets of correlated data can be efficiently analyzed under a unified framework of behavior recognition. We show that the correlation between different sets of data can be modeled as cooperation inside a team and that behavior recognition is a modern approach of classification and pattern recognition.
Biografía del autor
was born in Athens in 1981. He received his Engineering degree in 2004 and his PhD degree in 2007. Instead of spending most of his time in research, he is actively implied in ecology, self-construction using cob, cycling, renovative economical theories, strange musical instruments, martial arts and mostly, being in nature...
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Group Behavior Recognition using Dynamic BayesianNetworks
Verlag:
VDM Verlag Dr. Müller, 2009
ISBN 10: 3639126572
ISBN 13: 9783639126570
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Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gaitanis Konstantinos D.was born in Athens in 1981. He received his Engineering degree inn2004 and his PhD degree in 2007. Instead of spending most of hisntime in research, he is actively implied in ecology,nself-construction using c. Bestandsnummer des Verkäufers 4959847
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Group Behavior Recognition using Dynamic Bayesian Networks : Understanding intentions, goals and actions that take place inside teams
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this PhD thesis we analyze the concepts involvedin the decision making of groups of agents and applythese concepts in creating a framework for performinggroup behavior recognition. We present an overview ofthe intention theory, as studied by some greattheorists such as Searle, Bratmann and Cohen, andshow the link with more recent researches. We studythe advantages and drawbacks of some techniques inthe domain and create a new model for representingand detecting group behaviors, the aim being tocreate a unified approach of the problem. Most ofthis thesis is consecrated in the detailedpresentation of the model as well as the algorithmresponsible for behavior recognition. Our model istested on two different applications involving humangesture analysis and multimodal fusion of audio andvideo data. By means of these applications, weadvance the argument that multivariate sets ofcorrelated data can be efficiently analyzed under aunified framework of behavior recognition. We showthat the correlation between different sets of datacan be modeled as cooperation inside a team and thatbehavior recognition is a modern approach ofclassification and pattern recognition. Bestandsnummer des Verkäufers 9783639126570
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Group Behavior Recognition using Dynamic Bayesian Networks: Understanding intentions, goals and actions that take place inside teams
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Group Behavior Recognition using Dynamic Bayesian Networks Understanding intentions, goals and actions that take place inside teams
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Group Behavior Recognition using Dynamic Bayesian Networks: Understanding intentions, goals and actions that take place inside teams
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VDM Verlag Dr. M�ller 2009-02-25, 2009
ISBN 10: 3639126572
ISBN 13: 9783639126570
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Group Behavior Recognition using Dynamic Bayesian Networks: Understanding intentions, goals and actions that take place inside teams
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Group Behavior Recognition using Dynamic Bayesian Networks
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2009
ISBN 10: 3639126572
ISBN 13: 9783639126570
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Group Behavior Recognition using Dynamic Bayesian Networks
Verlag:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2009
ISBN 10: 3639126572
ISBN 13: 9783639126570
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Group Behavior Recognition using Dynamic Bayesian Networks
Verlag:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2009
ISBN 10: 3639126572
ISBN 13: 9783639126570
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