Statistical evaluation of dynamical interaction involving bees: bayesian tracking and mutual information
Autor(a) principal: | |
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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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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 |
_version_ |
1815257343969984512 |