Multi-people tracking using a distributed camera network: application to a university campus

Dissertação de mestrado em Engenharia de Telecomunicações e InformáticaNesta dissertação é explorado o tema da monitorização de múltiplos objetos no contexto de um ”smart campus”, com foco no contexto específico num campus universitário, sendo este o tema principal do projeto de investigação Lab4USpace. A monitorização de múltiplos objetos, especialmente de pessoas, é relevante para diversas aplicações, incluindo aplicações de vigilância, mobilidade e inteligência ambiental. No entanto, torna-se particularmente desafiante no contexto de espaços abertos, às quais exigem soluções com múltiplas câmaras com problemas inerentes, tais como a reidentificação. O objetivo desta dissertação é desenvolver um framework capaz de fornecer informações sobre o percurso de várias pessoas ao longo do campus universitário usando um cenário com múltiplas câmaras. A solução visa não só a monitorização de uma pessoa num único cenário, mas também em todo o campus, coberto por diversas câmaras com ou sem sobreposição. Esta dissertação discute os diversos desafios enfrentados durante o desenvolvimento deste projeto, incluindo preocupações com a privacidade e segurança dos utilizadores do campus. Com isso, optou-se por não enviar imagens para nenhuma aplicação, tratando apenas das informações estritamente retiradas da monitorização das pessoas. Um dos principais desafios foi desenvolver um framework que rastreie vários objetos num ambiente de um ”smart campus”, abordando desafios de espaços abertos e problemas de reidentificação. Além disso, devido aos recursos computacionais limitados, foi usado um computador de bordo para lidar com processamento de imagens e operações relacionadas às técnicas de visão computacional de maneira mais eficaz. O framework proposto utiliza modelos de deteção de objetos e algoritmos de monitorização em tempo real que foram comparados neste contexto específico. Depois de pesquisar outras alternativas, a estrutura usa o modelo YOLOv7-tiny para deteção de objetos, BoT-Sort para a monitorização dos vários objetos e Deep Person Reid para a reidentificação. O programa foi desenvolvido em Python e juntamente a ele foi também criado um website para alterar as configurações do sistema de monitorização utilizando o framework Flask. Um message broker também foi utilizado para a comunicação entre os diversos componentes do sistema. Os testes de validação demonstram a eficácia da framework proposta na monitorização das várias pessoas em todo o campus. O sistema proposto contribui significativamente para o desenvolvimento de soluções de múltiplas câmaras mais eficientes e eficazes para aplicações de ”smart campus”, com benefícios potenciais para a segurança, proteção e gestão do campus. No geral, esta dissertação apresenta uma estrutura que rastreia de maneira eficaz várias pessoas num ambiente de ”smart campus”. A framework é uma contribuição importante para o desenvolvimento na área do ”smart campus” e tem potencial para desenvolvimento futuro e aplicações para além do campus universitário.This dissertation explores the topic of object multi-tracking in the context of a smart campus, focusing on the specific context of a university campus, being the main topic of the Lab4USpace research project. Multi-tracking of objects, especially people, is relevant for different applications, including surveillance, mobility, and ambient intelligence. However, it becomes particularly challenging in open spaces, which require multi-camera solutions with inherent issues like re-identification. The objective of this dissertation is to develop a framework capable of providing information about the path of multiple people throughout the university campus using a multi-camera scenario. The solution aims not only to track a person in a single scenario but also over the entire campus, covered by various cameras with or without overlapping. This dissertation discusses the challenges faced during the development of this project, including concerns about the privacy and security of campus users. As a result, the decision was made not to send images for any application, dealing only with the information strictly retrieved from the tracking. One main challenge was developing a framework that tracks multiple objects in a smart campus environment, addressing the challenges of open spaces and re-identification issues. Additionally, due to limited computational resources, an edge computer was used to handle image processing and computer vision-related operations more effectively. The proposed framework uses different object detection models and real-time tracking algorithms that were compared in this specific context. After researching other alternatives, the framework uses the YOLOv7 tiny model for object detection, BoT-Sort for multiple object tracking, and Deep Person Reid for re-identification. The program was developed in Python and alongside it was also created a website to change the configurations of the tracking system using the Flask framework. A message broker was also used for communication between the various components of the system. Validation tests demonstrate the effectiveness of the proposed framework in tracking multiple people across the campus. The proposed framework significantly contributes to developing efficient and effective multi-camera solutions for smart campus applications, with potential benefits for campus safety, security, and management. Overall, this dissertation presents a framework that effectively tracks multiple people in a smart campus environment. The framework is an important contribution to the smart campus context and has the potential for future development and applications beyond the university campus

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

II Consenso Brasileiro de Tuberculose: Diretrizes Brasileiras para Tuberculose 2004

Universidade Federal de São Paulo (UNIFESP) Escola Paulista de MedicinaUniversidade Federal do Rio de Janeiro Instituto Doenças do TóraxRede TB de PesquisaMinistério da Saúde Centro de Referência Hélio Fraga Laboratório Nacional de BacteriologiaUniversidade Federal da BahiaSociedade Brasileira de Cirurgia TorácicaUniversidade de São Paulo Faculdade de Medicina de Ribeirão PretoUniversidade Federal do Rio de Janeiro IPPMGSociedade Brasileira de PediatriaUniversidade Federal do Espírito Santo Faculdade de Medicina Núcleo de Doenças InfecciosasSecretaria de Estado da Saúde da Paraíba Coordenação de TuberculoseSecretaria de Estado da Saúde da Bahia Hospital Otávio MangabeiraSecretaria de Estado da Saúde de São Paulo Instituto Clemente FerreiraFundação Ataulpho de PaivaMinistério da Saúde Secretaria de Vigilância em Saúde Programa Nacional de DST/AidsUniversidade Estadual do Rio de Janeiro Hospital Universitário Pedro ErnestoMinistério da Saúde Centro de Referência Hélio FragaUniversidade Federal do Rio Grande do NorteUniversidade Federal da Bahia Instituto de Saúde ColetivaPontifícia Universidade CatólicaUniversidade Estadual do Rio de JaneiroMinistério da Saúde Secretaria de Vigilância à Saúde Coordenação Geral de Doenças EndêmicasUniversidade de São Paulo Faculdade de Medicina Hospital das ClínicasSociedade Brasileira de Pneumologia e Tisiologia Comissão de TuberculoseUniversidade de São Paulo Faculdade de Saúde PúblicaFIOCRUZ Escola Nacional de Saúde PúblicaSecretaria Municipal da Saúde Hospital Municipal Raphael de Paula SouzaUniversidade Federal do Pará Hospital Universitário João de Barros Barreto BelémSecretaria de Estado da Saúde Hospital Sanatório Parthenon de Porto AlegreUniversidade Federal do ParanáSociedade Brasileira de Pneumologia e Tisiologia Comissão de InfecçõesSecretaria da Saúde do Município do Rio de Janeiro Coordenação de TuberculoseUniversidade Federal de Minas Gerais Faculdade de MedicinaSecretaria de Estado da Saúde de São Paulo Centro de Vigilância Epidemiológica Coordenação de TuberculoseSecretaria de Estado da Saúde de São Paulo Centro de Referência DST/AidsUniversidade de São Paulo Faculdade de MedicinaUNIFESP, EPMSciEL

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans—anteaters, sloths, and armadillos—have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora

Make EU trade with Brazil sustainable

Brazil, home to one of the planet's last great forests, is currently in trade negotiations with its second largest trading partner, the European Union (EU). We urge the EU to seize this critical opportunity to ensure that Brazil protects human rights and the environment