Behavior recognition from multiple views using fused hidden Markov models

Kosmopoulos, DI; Voulodimos, AS; Varvarigou, TA

dc.contributor.author	Kosmopoulos, DI	en
dc.contributor.author	Voulodimos, AS	en
dc.contributor.author	Varvarigou, TA	en
dc.date.accessioned	2014-03-01T02:46:42Z
dc.date.available	2014-03-01T02:46:42Z
dc.date.issued	2010	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32795
dc.subject	Behavior recognition	en
dc.subject	Hidden Markov models	en
dc.subject	Multi-camera classification	en
dc.subject.other	Behavior recognition	en
dc.subject.other	Gaussians	en
dc.subject.other	Human behaviors	en
dc.subject.other	Industrial environments	en
dc.subject.other	Limited visibility	en
dc.subject.other	Motion history images	en
dc.subject.other	Multi-cameras	en
dc.subject.other	Multiple cameras	en
dc.subject.other	Multiple views	en
dc.subject.other	Target recognition	en
dc.subject.other	Visual behavior	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Behavioral research	en
dc.subject.other	Cameras	en
dc.subject.other	Feature extraction	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Video cameras	en
dc.subject.other	Hidden Markov models	en
dc.title	Behavior recognition from multiple views using fused hidden Markov models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-12842-4_41	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-12842-4_41	en
heal.publicationDate	2010	en
heal.abstract	In this work, we provide a framework for recognizing human behavior from multiple cameras in structured industrial environments. Since target recognition and tracking can be very challenging, we bypass these problems by employing an approach similar to Motion History Images for feature extraction. Modeling and recognition are performed through the use of Hidden Markov Models (HMMs) with Gaussian observation likelihoods. The problems of limited visibility and occlusions are addressed by showing how the framework can be extended for multiple cameras, both at the feature and at the state level. Finally, we evaluate the performance of the examined approaches under real-life visual behavior understanding scenarios and we discuss the obtained results. © Springer-Verlag Berlin Heidelberg 2010.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-12842-4_41	en
dc.identifier.volume	6040 LNAI	en
dc.identifier.spage	345	en
dc.identifier.epage	350	en