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Human activity monitoring by local and global finite state machines

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dc.contributor.author Castillo Montoya, José Carlos es_ES
dc.contributor.author Rodríguez Sánchez, José María es_ES
dc.contributor.author Fernández Caballero, Antonio es_ES
dc.date.accessioned 2012-03-20T07:11:31Z
dc.date.available 2012-03-20T07:11:31Z
dc.date.issued 2012 es_ES
dc.identifier.citation Expert systems with applications, 2012, 39(8): 6982-6993 es_ES
dc.identifier.issn 0957-4174 es_ES
dc.identifier.uri http://hdl.handle.net/10578/2113
dc.description.abstract There are a number of solutions to automate the monotonous task of looking at a monitor to find suspicious behaviors in video surveillance scenarios. Detecting strange objects and intruders, or tracking people and objects, is essential for surveillance and safety in crowded environments. The present work deals with the idea of jointly modeling simple and complex behaviors to report local and global human activities in natural scenes. Modeling human activities with state machines is still common in our days and is the approach offered in this paper. We incorporate knowledge about the problem domain into an expected structure of the activity model. Motion-based image features are linked explicitly to a symbolic notion of hierarchical activity through several layers of more abstract activity descriptions. Atomic actions are detected at a low level and fed to hand-crafted grammars to detect activity patterns of interest. Also, we work with shape and trajectory to indicate the events related to moving objects. In order to validate our proposal we have performed several tests with some CAVIAR test cases. es_ES
dc.format text/plain en_US
dc.language.iso es en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Ingenierías es_ES
dc.title Human activity monitoring by local and global finite state machines es_ES
dc.type info:eu-repo/semantics/article en_US


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