A learning approach to swarm-based path detection and tracking

Detalhes bibliográficos
Autor(a) principal: Mendonça, Ricardo André Martins
Data de Publicação: 2012
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/8226
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
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spelling A learning approach to swarm-based path detection and trackingSwarm cognitionMonocular path detectionVisual saliencyBio-inspired methodsOff-road navigationDissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThis dissertation presents a set of top-down modulation mechanisms for the modulation of the swarm-based visual saliency computation process proposed by Santana et al. (2010) in context of path detection and tracking. In the original visual saliency computation process, two swarms of agents sensitive to bottom-up conspicuity information interact via pheromone-like signals so as to converge on the most likely location of the path being sought. The behaviours ruling the agents’motion are composed of a set of perception-action rules that embed top-down knowledge about the path’s overall layout. This reduces ambiguity in the face of distractors. However, distractors with a shape similar to the one of the path being sought can still misguide the system. To mitigate this issue, this dissertation proposes the use of a contrast model to modulate the conspicuity computation and the use of an appearance model to modulate the pheromone deployment. Given the heterogeneity of the paths, these models are learnt online. Using in a modulation context and not in a direct image processing, the complexity of these models can be reduced without hampering robustness. The result is a system computationally parsimonious with a work frequency of 20 Hz. Experimental results obtained from a data set encompassing 39 diverse videos show the ability of the proposed model to localise the path in 98.67 % of the 29789 evaluated frames.Faculdade de Ciências e TecnologiaOliveira, JoséSantana, PedroRUNMendonça, Ricardo André Martins2012-11-29T15:08:03Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/8226enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T03:40:45Zoai:run.unl.pt:10362/8226Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:18:04.100731Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A learning approach to swarm-based path detection and tracking
title A learning approach to swarm-based path detection and tracking
spellingShingle A learning approach to swarm-based path detection and tracking
Mendonça, Ricardo André Martins
Swarm cognition
Monocular path detection
Visual saliency
Bio-inspired methods
Off-road navigation
title_short A learning approach to swarm-based path detection and tracking
title_full A learning approach to swarm-based path detection and tracking
title_fullStr A learning approach to swarm-based path detection and tracking
title_full_unstemmed A learning approach to swarm-based path detection and tracking
title_sort A learning approach to swarm-based path detection and tracking
author Mendonça, Ricardo André Martins
author_facet Mendonça, Ricardo André Martins
author_role author
dc.contributor.none.fl_str_mv Oliveira, José
Santana, Pedro
RUN
dc.contributor.author.fl_str_mv Mendonça, Ricardo André Martins
dc.subject.por.fl_str_mv Swarm cognition
Monocular path detection
Visual saliency
Bio-inspired methods
Off-road navigation
topic Swarm cognition
Monocular path detection
Visual saliency
Bio-inspired methods
Off-road navigation
description Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
publishDate 2012
dc.date.none.fl_str_mv 2012-11-29T15:08:03Z
2012
2012-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/8226
url http://hdl.handle.net/10362/8226
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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