Multi-camera framework for object detection and distance estimation

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.Os veículos autônomos podem reduzir o número de acidentes automobilísticos e o número de vítimas fatais. Segundo o Departamento de Estatística da Alemanha, apenas em 2019, ocorreram mais de 2 milhões de acidentes de carro. Este trabalho propõe um sistema multi câmera para detecção de objetos e medição de distâncias por visão computacional que irá apoiar alguns testes de veículos autônomos e melhorar a segurança durante os testes. Três abordagens são realizadas e comparadas com a distância real, e apenas a melhor técnica foi incluída no framework proposto. Na maioria dos casos, esse erro está diretamente relacionado a fatores meteorológicos e sinais de comunicação fracos entre as câmeras e o hardware de controle. Os resultados obtidos mostram que os métodos de detecção de objetos garantem precisão com exatidão acima de 93 % em condições ideais e ambientes controlados. No entanto, a precisão é reduzida quando os obstáculos estão presentes na frente do objeto detectado. Técnicas adicionais também são propostas para otimizar o posicionamento das câmeras e o ângulo de inclinação.Autonomous Vehicles can reduce the number of car crashes and the number of fatal victims. Following the German Statistical Department, just in 2019, there were over 2 million car accidents, and more than 90 percent of crashes are caused by human errors (National Highway Traffic Safety Administration, 2015). This work proposes a multi-camera system for object detection and distance measurement using computer vision that it will support some autonomous vehicle tests and improve safety during the tests. Three approaches are performed and compared with the real distance, and just the best technique was included in the proposed framework. In most cases, this error is directly related to meteorological factors and weak communication signals between cameras and the control hardware. The results obtained show that object detection methods guarantee precision with an accuracy above 93 % in ideal conditions and controlled environments. However, accuracy is reduced when obstacles are present in front of the detected object. Additional techniques are also proposed to optimize the positioning of the cameras and the angle of inclination

Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC

abstract: Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC.View the article as published at https://www.hindawi.com/journals/tswj/2014/189481

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC

Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC

Image analysis platforms for exploring genetic and neuronal mechanisms regulating animal behavior

An important aim of neuroscience is to understand how gene interactions and neuronal networks regulate animal behavior. The larvae of the marine annelid Platynereis dumerilii provide a convenient system for such integrative studies. These larvae exhibit a wide range of behaviors, including phototaxis, chemotaxis and gravitaxis and at the same time exhibit relatively simple nervous system organization. Due to its small size and transparent body, the Platynereis larva is compatible with whole-body light microscopic imaging following tissue staining protocols. It is also suitable for serial electron microscopic imaging and subsequent neuronal connectome reconstruction. Despite advances in imaging techniques, automated computational tools for large data analysis are not well-established in Platynereis. In the current work, I developed image analysis software for exploring genetic and nervous system mechanisms modulating Platynereis behavior. Exploring gene expression patterns Current labeling and imaging techniques restrict the number of gene expression patterns that can be labelled and visualized in a single specimen, which hinders the study of behaviors driven by multi-molecular interactions. To address this problem, I employed image registration to generate a gene expression atlas that integrates gene expression information from multiple specimens in a common reference space. The gene expression atlas was used to investigate mechanisms regulating larval locomotion, settlement and phototaxis in Platynereis. The atlas can assist in the identification of inter-individual and inter-species variations in gene expression. To provide a representation convenient for exploring gene expression patterns, I created a model of the atlas using 3D graphics software, which enabled convenient data visualization and efficient data storage and sharing. Exploring neuronal networks regulating behavior Neuronal circuitry can be reconstructed from the images obtained from electron microscopy, which resolves very fine structures such as neuron morphology or synapses. The amount of data resulting from electron microscopy and the complexity of neuronal networks represent a significant challenge for manual analysis. To solve this problem, I developed the NeuroDetective software, which models a neuronal circuitry and analyzes the information flow within it. The software combines the advantages of 3D visualization and graph analysis software by integrating neuron morphology and spatial distribution together with synaptic connectivity. NeuroDetective allowed studying the neuronal circuitry responsible for phototaxis in Platynereis larvae, revealing the connections and the neurons important for the network functionality. NeuroDetective facilitated the establishment of a relationship between the function and the structure of the neuronal circuitry in Platynereis phototaxis. Integrating gene expression patterns with neuronal connectivity Neuronal circuitry and its associated modulating biomolecules, such as neurotransmitters and neuropeptides, are thought to be the main factors regulating animal behavior. Therefore it was important to integrate both genetic and neuronal information in order to fully understand how biomolecules in conjunction with neuronal anatomy elicit certain animal behavior. To resolve the difference in specimen preparation for gene expression versus electron microscopy preparations, I developed an image registration procedure to match the signals from these two different datasets. This method enabled the integration the spatial distribution of specific modulators into the analysis of neuronal networks, leading to an improved understanding of the genetic and neuronal mechanisms modulating behavior in Platynereis

Cyber-Human Systems, Space Technologies, and Threats

CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

Boletín estadístico e indicadores bibliométricos de las revistas integradas al Portal de Revistas de la UTP

El boletín estadístico e indicadores bibliométricos de las revistas integradas al Portal de Revistas de la UTP, muestra los resultados obtenidos del proyecto # UC7003817, Implementación de un Portal de Revistas Digitales en la Universidad Tecnológica de Panamá para mejorar la visibilidad, alcance y medir el impacto de las publicaciones a través de métricas. Vigencia: 2016 - 2020 Objetivo General Implementar una plataforma tecnológica de revistas utilizando un software que permita la gestión editorial, catalogación y el acceso abierto de sus publicaciones con el fin mejorar la visibilidad en buscadores especializados, mayor alcance al integrar las revistas en directorios, bases de datos y repositorios nacionales e internacionales a través de la interoperabilidad de la plataforma con el fin de medir el impacto y visitas de sus contenidos. Área de Investigación: Tecnologías de la Información y Comunicaciones Programa: Investigación y Desarrollo (I+D) Investigadores: Danny Murillo - Investigador Principal (IP) Dalys Johana Saavedra Villarreal - Co-Investigador (Co-IP) Casilda Saavedra Hernández - Co-Investigador (Co-IP) Madelaine Fernández - Co-Investigador (Co-IP) Angel Moulanier - Co-Investigador (Co-IP) Libia Batista (Investigador Colaborador)El boletín estadístico e indicadores bibliométricos de las revistas integradas al Portal de Revistas de la UTP, muestra los resultados obtenidos del proyecto # UC7003817, Implementación de un Portal de Revistas Digitales en la Universidad Tecnológica de Panamá para mejorar la visibilidad, alcance y medir el impacto de las publicaciones a través de métricas. Vigencia: 2016 - 2020 Objetivo General Implementar una plataforma tecnológica de revistas utilizando un software que permita la gestión editorial, catalogación y el acceso abierto de sus publicaciones con el fin mejorar la visibilidad en buscadores especializados, mayor alcance al integrar las revistas en directorios, bases de datos y repositorios nacionales e internacionales a través de la interoperabilidad de la plataforma con el fin de medir el impacto y visitas de sus contenidos. Área de Investigación: Tecnologías de la Información y Comunicaciones Programa: Investigación y Desarrollo (I+D) Investigadores: Danny Murillo - Investigador Principal (IP) Dalys Johana Saavedra Villarreal - Co-Investigador (Co-IP) Casilda Saavedra Hernández - Co-Investigador (Co-IP) Madelaine Fernández - Co-Investigador (Co-IP) Angel Moulanier - Co-Investigador (Co-IP) Libia Batista (Investigador Colaborador