Real-time accumulative computation motion detectors

The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machine...

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Detalles Bibliográficos
Autores: Maldonado Bascón, Saturnino, Fernández Caballero, Antonio, Castillo Montoya, José Carlos, López Bonal, María Teresa
Tipo de recurso: artículo
Fecha de publicación:2009
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/2126
Acceso en línea:http://hdl.handle.net/10578/2126
Access Level:acceso abierto
Palabra clave:Ingenierías
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spelling Real-time accumulative computation motion detectorsMaldonado Bascón, SaturninoFernández Caballero, AntonioCastillo Montoya, José CarlosLópez Bonal, María TeresaIngenieríasThe neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a) A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively.201220122009info:eu-repo/semantics/articletext/plainapplication/pdfhttp://hdl.handle.net/10578/2126reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaEspañolinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/21262026-05-27T07:36:41Z
dc.title.none.fl_str_mv Real-time accumulative computation motion detectors
title Real-time accumulative computation motion detectors
spellingShingle Real-time accumulative computation motion detectors
Maldonado Bascón, Saturnino
Ingenierías
title_short Real-time accumulative computation motion detectors
title_full Real-time accumulative computation motion detectors
title_fullStr Real-time accumulative computation motion detectors
title_full_unstemmed Real-time accumulative computation motion detectors
title_sort Real-time accumulative computation motion detectors
dc.creator.none.fl_str_mv Maldonado Bascón, Saturnino
Fernández Caballero, Antonio
Castillo Montoya, José Carlos
López Bonal, María Teresa
author Maldonado Bascón, Saturnino
author_facet Maldonado Bascón, Saturnino
Fernández Caballero, Antonio
Castillo Montoya, José Carlos
López Bonal, María Teresa
author_role author
author2 Fernández Caballero, Antonio
Castillo Montoya, José Carlos
López Bonal, María Teresa
author2_role author
author
author
dc.subject.none.fl_str_mv Ingenierías
topic Ingenierías
description The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a) A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively.
publishDate 2009
dc.date.none.fl_str_mv 2009
2012
2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10578/2126
url http://hdl.handle.net/10578/2126
dc.language.none.fl_str_mv Español
language_invalid_str_mv Español
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/plain
application/pdf
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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