Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information

Detalhes bibliográficos
Autor(a) principal: Oliveira Júnior, Jordão Natal de
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
Texto Completo: https://www.teses.usp.br/teses/disponiveis/18/18153/tde-25102021-102129/
Resumo: Tracking objects in video is a cheap method to obtain information about the parts of a system. However, when there are many objects simultaneously in the tracking some problems can happen, such as overlapping and swap of labels, compromising the overall efficiency. Recently new approaches for solving these problems were developed e.g. Convolutional Neural Networks, but the computational cost is still very high. Here, a Bayesian tracking algorithm to supervise objects on video frames is described. The algorithm allows the evaluation of and Probability Distribution Function (PDF) of the objects being tracked by combining the tracking with the Kernel Density Estimation (KDE). The proposed algorithm was evaluated through simulation and comparison with similar approaches, since the conventional databases (as Princeton Tracking Benchmark) lacks similarity with the problem of the one approached in this dissertation. The algorithm is able to track the objects with great precision, thus being able to dynamically evaluate the entropy and energy, by using polar coordinates and assuming a von Mises distribution for the angle variation prediction and a non-informative distribution for the radius prediction. Then, with the information obtained from the algorithm, a resilience analysis was made approaching the effects of two agrochemicals in the honey bees: the insecticide imidacloprid and the fungicide cerconil. Additional information about how they affect honey bees was obtained via Mutual Information on lethal doses, reinforcing the previous results.
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spelling Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual informationAvaliação estatística de interações dinâmicas envolvendo abelhas: rastreamento bayesiano e informação mútuaAbelhas de abelhasAgrochemicalsAgroquímicosBayesian inferenceBee swarm Honey beesInferência bayesianaMulti-target trackingRastreamento de múltiplos alvosTracking objects in video is a cheap method to obtain information about the parts of a system. However, when there are many objects simultaneously in the tracking some problems can happen, such as overlapping and swap of labels, compromising the overall efficiency. Recently new approaches for solving these problems were developed e.g. Convolutional Neural Networks, but the computational cost is still very high. Here, a Bayesian tracking algorithm to supervise objects on video frames is described. The algorithm allows the evaluation of and Probability Distribution Function (PDF) of the objects being tracked by combining the tracking with the Kernel Density Estimation (KDE). The proposed algorithm was evaluated through simulation and comparison with similar approaches, since the conventional databases (as Princeton Tracking Benchmark) lacks similarity with the problem of the one approached in this dissertation. The algorithm is able to track the objects with great precision, thus being able to dynamically evaluate the entropy and energy, by using polar coordinates and assuming a von Mises distribution for the angle variation prediction and a non-informative distribution for the radius prediction. Then, with the information obtained from the algorithm, a resilience analysis was made approaching the effects of two agrochemicals in the honey bees: the insecticide imidacloprid and the fungicide cerconil. Additional information about how they affect honey bees was obtained via Mutual Information on lethal doses, reinforcing the previous results.Rastrear objetos em vídeo é um método barato para obter informações sobre as partes de um sistema. No entanto, quando há muitos objetos simultaneamente no rastreamento, alguns problemas podem ocorrer, como sobreposição e troca de rótulos, comprometendo a eficiência geral. Recentemente, novas abordagens para resolver estes problemas foram desenvolvidas, por exemplo, Redes Neurais Convulacionais, mas o custo computacional ainda é muito alto. Neste trabalho foi desenvolvido um algoritmo de rastreamento Bayesiano para monitorar objetos em quadros de vídeo. O algoritmo permite a avaliação da Função de Distribuição de Probabilidade (PDF) dos objetos que estão sendo rastreados, combinando o rastreamento com o KDE (Kernel Density Estimation). O algoritmo proposto foi avaliado através de simulação e comparação com abordagens semelhantes, uma vez que as bases de dados convencionais (Princeton Tracking Benchmark) não apresentam semelhança com o problema daquele abordado nesta dissertação. O algoritmo é capaz de rastrear os objetos com grande precisão, sendo capaz de avaliar dinamicamente a entropia e energia, usando coordenadas polares e assumindo uma distribuição de Mises para a previsão de variação de ângulo e uma distribuição não informativa para a predição de raio. Em seguida, com as informações obtidas a partir do algoritmo, foi feita a análise de resiliência abordando os efeitos de dois agroquímicos nas abelhas: o inseticida imidaclopride e o fungicida cerconil. Informações adicionais sobre como elas afetam as abelhas foram obtidas através de Informações Mútuas sobre doses letais, reforçando os resultados anteriores.Biblioteca Digitais de Teses e Dissertações da USPMaciel, Carlos DiasOliveira Júnior, Jordão Natal de 2019-07-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/18/18153/tde-25102021-102129/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2021-11-11T11:13:02Zoai:teses.usp.br:tde-25102021-102129Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212021-11-11T11:13:02Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
Avaliação estatística de interações dinâmicas envolvendo abelhas: rastreamento bayesiano e informação mútua
title Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
spellingShingle Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
Oliveira Júnior, Jordão Natal de
Abelhas de abelhas
Agrochemicals
Agroquímicos
Bayesian inference
Bee swarm Honey bees
Inferência bayesiana
Multi-target tracking
Rastreamento de múltiplos alvos
title_short Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
title_full Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
title_fullStr Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
title_full_unstemmed Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
title_sort Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
author Oliveira Júnior, Jordão Natal de
author_facet Oliveira Júnior, Jordão Natal de
author_role author
dc.contributor.none.fl_str_mv Maciel, Carlos Dias
dc.contributor.author.fl_str_mv Oliveira Júnior, Jordão Natal de
dc.subject.por.fl_str_mv Abelhas de abelhas
Agrochemicals
Agroquímicos
Bayesian inference
Bee swarm Honey bees
Inferência bayesiana
Multi-target tracking
Rastreamento de múltiplos alvos
topic Abelhas de abelhas
Agrochemicals
Agroquímicos
Bayesian inference
Bee swarm Honey bees
Inferência bayesiana
Multi-target tracking
Rastreamento de múltiplos alvos
description Tracking objects in video is a cheap method to obtain information about the parts of a system. However, when there are many objects simultaneously in the tracking some problems can happen, such as overlapping and swap of labels, compromising the overall efficiency. Recently new approaches for solving these problems were developed e.g. Convolutional Neural Networks, but the computational cost is still very high. Here, a Bayesian tracking algorithm to supervise objects on video frames is described. The algorithm allows the evaluation of and Probability Distribution Function (PDF) of the objects being tracked by combining the tracking with the Kernel Density Estimation (KDE). The proposed algorithm was evaluated through simulation and comparison with similar approaches, since the conventional databases (as Princeton Tracking Benchmark) lacks similarity with the problem of the one approached in this dissertation. The algorithm is able to track the objects with great precision, thus being able to dynamically evaluate the entropy and energy, by using polar coordinates and assuming a von Mises distribution for the angle variation prediction and a non-informative distribution for the radius prediction. Then, with the information obtained from the algorithm, a resilience analysis was made approaching the effects of two agrochemicals in the honey bees: the insecticide imidacloprid and the fungicide cerconil. Additional information about how they affect honey bees was obtained via Mutual Information on lethal doses, reinforcing the previous results.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-25
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 https://www.teses.usp.br/teses/disponiveis/18/18153/tde-25102021-102129/
url https://www.teses.usp.br/teses/disponiveis/18/18153/tde-25102021-102129/
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv
dc.rights.driver.fl_str_mv Liberar o conteúdo para acesso público.
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Liberar o conteúdo para acesso público.
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv
dc.publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
dc.source.none.fl_str_mv
reponame:Biblioteca Digital de Teses e Dissertações da USP
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Biblioteca Digital de Teses e Dissertações da USP
collection Biblioteca Digital de Teses e Dissertações da USP
repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)
repository.mail.fl_str_mv virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br
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