<p>This thesis project was carried out at Volvo Car Corporation. It is based on an EU project called Locust in which a bio-inspired visual sensor system (the Locust sensor system) for automotive collision avoidance was developed. The Locust sensor system is designed to emulate the collision avoidance functionality of the Locust grasshopper, which is well-known for its extraordinary vision based collision avoidance ability, in particular with regard to its fast reaction times to perceived threats. Volvo Car Corporation is interested in the possibility of using the bio-inspired technology developed in the Locust project to improve its already existing collision avoidance systems. Pedestrian collision avoidance is of high interest, for which the properties of the Locust grasshopper are desirable.</p><p>The purpose of this thesis project is to develop two demonstrator vehicles to test the performance of the Locust sensor system, carry out the testing, and evaluate its usability for Volvo Car Corporation. The first vehicle is a scale 1:5 model car that was originally developed in a thesis project at KTH, and the second a full scale Volvo XC90.</p><p>It was found in the testing that the Locust sensor system is promising for pedestrian collision avoidance applications. The results for detecting other vehicles were also acceptable, but Volvo Car Corporation already has other collision avoidance systems with better performance in this regard. In general the test results were very good for speeds up to about 40 km/h. This indicates that the Locust sensor system would be most usable in a city driving environment, parking lot situations, and for driving in residential areas.</p>

This thesis project was carried out at Volvo Car Corporation. It is based on an EU project called Locust in which a bio-inspired visual sensor system (the Locust sensor system) for automotive collision avoidance was developed. The Locust sensor system is designed to emulate the collision avoidance functionality of the Locust grasshopper, which is well-known for its extraordinary vision based collision avoidance ability, in particular with regard to its fast reaction times to perceived threats. Volvo Car Corporation is interested in the possibility of using the bio-inspired technology developed in the Locust project to improve its already existing collision avoidance systems. Pedestrian collision avoidance is of high interest, for which the properties of the Locust grasshopper are desirable. The purpose of this thesis project is to develop two demonstrator vehicles to test the performance of the Locust sensor system, carry out the testing, and evaluate its usability for Volvo Car Corporation. The first vehicle is a scale 1:5 model car that was originally developed in a thesis project at KTH, and the second a full scale Volvo XC90. It was found in the testing that the Locust sensor system is promising for pedestrian collision avoidance applications. The results for detecting other vehicles were also acceptable, but Volvo Car Corporation already has other collision avoidance systems with better performance in this regard. In general the test results were very good for speeds up to about 40 km/h. This indicates that the Locust sensor system would be most usable in a city driving environment, parking lot situations, and for driving in residential areas.

Deployment of cameras in Wireless Multimedia Sensor Networks (WMSNs) is crucial in achieving good coverage, accuracy and fault tolerance. With the decreased costs of wireless cameras, WMSNs provide opportunities for redundant camera deployment in order to get multiple disparate views of events. Referred to as multi-perspective coverage (MPC), this thesis proposes an optimal solution for camera deployment that can achieve full MPC for a given region. The solution is based on a Bi-Level mixed integer program (MIP) which works by solving two sub-problems named master and sub-problems. The master problem identifies a solution based on an initial set of points and then calls the sub-problem to cover the uncovered points iteratively. The Bi-Level algorithm is then revised to provide MPC with the minimum cost in Heteregeneous Visual Sensor Networks (VSNs) where cameras may have different price, resolution, Field-of-View (FoV) and Depth-of-Field (DoF). For a given average resolution, area, and variety of camera sensors, we propose a deployment algorithm which minimizes the total cost while guaranteeing 100\% MPC of the area and a minimum resolution. Furthermore, revised Bi-level algorithm provides the flexibility of achieving required resolution on sub-regions for a given region. The numerical results show the superiority of our approach with respect to existing approaches.

Yes / In wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos inhibits the ability of analysts to extract actionable intelligence. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs. The proposed framework selects semantically relevant information before it is transmitted to a sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates the background by a bootstrapping procedure, thus increasing the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified as being high or low priority. This classification is dynamic, such that camera nodes toggle between high-priority and low-priority status depending on the coverage of the region of interest. High-priority camera nodes are allowed to access reliable radio channels to ensure the timely and reliable transmission of data. We compare the performance of this framework with other state-of-the-art methods for both single and multi-camera monitoring. The results demonstrate the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information. / Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904).

The use of visual sensors in providing the necessary information for the autonomous guidance and navigation of the unmanned-air vehicles (UAV) or micro-air vehicles (MAV) applications is inspired by biological systems and is motivated first of all by the reduction of the navigational sensor cost. Also, visual sensors can be more advantageous in military operations since they are difficult to detect. However, the design of a reliable guidance, navigation and control system for aerial vehicles based only on visual information has many unsolved problems, ranging from hardware/software development to pure control-theoretical issues, which are even more complicated when applied to the tracking of maneuvering unknown targets. This dissertation describes guidance law design and implementation algorithms for autonomous tracking of a flying target, when the information about the target's current position is obtained via a monocular camera mounted on the tracking UAV (follower). The visual information is related to the target's relative position in the follower's body frame via the target's apparent size, which is assumed to be constant, but otherwise unknown to the follower. The formulation of the relative dynamics in the inertial frame requires the knowledge of the follower's orientation angles, which are assumed to be known. No information is assumed to be available about the target's dynamics. The follower's objective is to maintain a desired relative position irrespective of the target's motion. Two types of guidance laws are designed and implemented in the dissertation. The first one is a smooth guidance law that guarantees asymptotic tracking of a target, the velocity of which is viewed as a time-varying disturbance, the change in magnitude of which has a bounded integral. The second one is a smooth approximation of a discontinuous guidance law that guarantees bounded tracking with adjustable bounds when the target's acceleration is viewed as a bounded but otherwise unknown time-varying disturbance. In both cases, in order to meet the objective, an intelligent excitation signal is added to the reference commands. These guidance laws are modified to accommodate measurement noise, which is inherently available when using visual sensors and image processing algorithms associated with them. They are implemented on a full scale non-linear aircraft model using conventional block backstepping technique augmented with a neural network for approximation of modeling uncertainties and atmospheric turbulence resulting from the closed-coupled flight of two aerial vehicles. / Ph. D.

No description available.

<div>Numerous organizations around the world deploy sensor networks, especially visual sensor networks for various applications like monitoring traffic, security, and emergencies. With advances in computer vision technology, the potential application of these sensor networks has expanded. This has led to an increase in demand for deployment of large scale sensor networks.</div><div>Sensors in a large network have differences in location, position, hardware, etc. These differences lead to varying usefulness as they provide different quality of information. As an example, consider the cameras deployed by the Department of Transportation (DOT). We want to know whether the same traffic cameras could be used for monitoring the damage by a hurricane.</div><div>Presently, significant manual effort is required to identify useful sensors for different applications. There does not exist an automated system which determines the usefulness of the sensors based on the application. Previous methods on visual sensor networks focus on finding the dependability of sensors based on only the infrastructural and system issues like network congestion, battery failures, hardware failures, etc. These methods do not consider the quality of information from the sensor network. In this paper, we present an automated system which identifies the most useful sensors in a network for a given application. We evaluate our system on 2,500 real-time live sensors from four cities for traffic monitoring and people counting applications. We compare the result of our automated system with the manual score for each camera.</div><div>The results suggest that the proposed system reliably finds useful sensors and it output matches the manual scoring system. It also shows that a camera network deployed for a certain application can also be useful for another application.</div>

Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications in large surveillance areas. Although potentially powerful, VSNs also present unique challenges that could hinder their practical deployment because of the unique camera features including the extremely higher data rate, the directional sensing characteristics, and the existence of visual occlusions. In this dissertation, we first present a collaborative approach for target localization in VSNs. Traditionally; the problem is solved by localizing targets at the intersections of the back-projected 2D cones of each target. However, the existence of visual occlusions among targets would generate many false alarms. Instead of resolving the uncertainty about target existence at the intersections, we identify and study the non-occupied areas in 2D cones and generate the so-called certainty map of targets non-existence. We also propose distributed integration of local certainty maps by following a dynamic itinerary where the entire map is progressively clarified. The accuracy of target localization is affected by the existence of faulty nodes in VSNs. Therefore, we present the design of a fault-tolerant localization algorithm that would not only accurately localize targets but also detect the faults in camera orientations, tolerate these errors and further correct them before they cascade. Based on the locations of detected targets in the fault-tolerated final certainty map, we construct a generative image model that estimates the camera orientations, detect inaccuracies and correct them. In order to ensure the required visual coverage to accurately localize targets or tolerate the faulty nodes, we need to calculate the coverage before deploying sensors. Therefore, we derive the closed-form solution for the coverage estimation based on the "certainty-based detection" model that takes directional sensing of cameras and existence of visual occlusions into account. The effectiveness of the proposed collaborative and fault-tolerant target localization algorithms in localization accuracy as well as fault detection and correction performance has been validated through the results obtained from both simulation and real experiments. In addition, conducted simulation shows extreme consistency with results from theoretical closed-form solution for visual coverage estimation, especially when considering the boundary effect.

Submitted by Ricardo Cedraz Duque Moliterno (ricardo.moliterno@uefs.br) on 2015-10-20T23:41:57Z No. of bitstreams: 1 Disserta??o-Danilo-Vers?o-final-p?s-banca .pdf: 11564872 bytes, checksum: 0a0388f802ddac466fce74bea4b02895 (MD5) / Made available in DSpace on 2015-10-20T23:41:57Z (GMT). No. of bitstreams: 1 Disserta??o-Danilo-Vers?o-final-p?s-banca .pdf: 11564872 bytes, checksum: 0a0388f802ddac466fce74bea4b02895 (MD5) Previous issue date: 2015-08-19 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Recently Wireless Sensor Networks have gained attention of researchers and industry around the world, such that many projects and solutions have been developed for various scenarios and applications. Such networks are formed by small sensor nodes with low processing power, few memory and few energy. Thus, resources are scarce, particularly energy, where, in most cases these nodes are powered by batteries, which is a crucial point in the network design. A kind of sensor network in which camera-enabled sensors are inserted are call Wireless Visual Sensor Networks. Because of this, these networks become able to recover large quantities of environment information which may to be interesting for several applications. However, in general, sensor networks are very vulnerable due to the nature of the communication and due also to the sensor nodes are, sometimes, in remote, hostile and hard to reach areas. Moreover, the sensor nodes are potentially inexpensive devices that can be easily purchased or designed by others to attack the network. So to mitigate these vulnerabilities, research in security area for such networks are required. However, traditional security mechanisms lead to very overhead of computing and communication can compromise the network performance when they are adopted. Thinking about it, this master's thesis aims to propose a new paradigm to ensure security for wireless visual sensor networks, being presented through a theoretical mathematical model to perform differentiation of areas in the monitoring environment to then considering the particularities of the application monitoring to provide security at different levels. Called Adaptive Encryption, this theoretical model can be used for various applications requiring different security assurances for different network locations, implying providing security at acceptable levels while consuming less network resources, above all energy. / Recentemente as Redes de Sensores Sem Fio t?m ganhado a aten??o de pesquisadores, da ind?stria e do meio acad?mico ao redor do mundo todo, de modo que muitos projetos e solu??es t?m sido desenvolvidas para diversos cen?rios e aplica??es. Essas redes s?o formadas por pequenos n?s sensores com pouco poder de processamento, mem?ria e energia. Sendo assim, os recursos s?o bastante escassos, principalmente energia, onde, na maioria das vezes estes n?s s?o alimentados por baterias, sendo este um ponto crucial no projeto da rede. Um tipo de rede de sensores em que os n?s possuem c?meras de v?deo embutidas s?o chamadas de Redes de Sensores Visuais Sem Fio. Devido a isso, tais redes se tornam capazes de recuperar grandes quantidades de informa??es do ambiente o que pode ser interessante para diversas aplica??es. Todavia, de forma geral, as redes de sensores s?o muito vulner?veis devido a natureza da comunica??o e devido tamb?m aos n?s sensores estarem, algumas vezes, em locais remotos, hostis e de dif?cil acesso. Al?m disso, os n?s sensores s?o dispositivos potencialmente baratos que podem ser facilmente adquiridos ou projetados por terceiros a fim de atacar a rede. Ent?o, visando atenuar essas vulnerabilidades, pesquisas na ?rea de seguran?a para tais redes s?o necess?rias. Contudo, os mecanismos de seguran?a tradicionais geram muito sobrecarga de computa??o e comunica??o podendo comprometer o desempenho da rede quando s?o adotados. Pensando nisso, este trabalho de mestrado tem como objetivo propor um novo paradigma para garantir seguran?a para redes de sensores visuais sem fio, sendo apresentado atrav?s um modelo matem?tico te?rico para realizar diferencia??o de ?reas no ambiente de monitoramento para, ent?o, considerando as particularidades da aplica??o de monitoramento, prover seguran?a em diferentes n?veis. Chamado de Criptografia Adaptativa, este modelo te?rico pode ser utilizado por diversas aplica??es que necessitem de garantias de seguran?a diferenciadas para diferentes locais da rede, o que implica em prover seguran?a em n?veis aceit?veis consumindo menos recursos da rede, principalmente energia.

Submitted by Jadson Francisco de Jesus SILVA (jadson@uefs.br) on 2018-07-18T21:55:12Z No. of bitstreams: 1 Disserta??o.pdf: 2639155 bytes, checksum: af49bdcdf83d4a063546324a223124a4 (MD5) / Made available in DSpace on 2018-07-18T21:55:12Z (GMT). No. of bitstreams: 1 Disserta??o.pdf: 2639155 bytes, checksum: af49bdcdf83d4a063546324a223124a4 (MD5) Previous issue date: 2018-03-27 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES / Wireless visual sensor networks can provide valuable information for a lot of moni- toring and control applications, which has driven much attention from the academic community in last years. For some applications, a set of targets have to be covered by visual sensors and sensing redundancy may be desired in many cases, especially when applications have availability requirements or demands for multiple coverage perspectives for viewed targets. For rotatable visual sensors, the sensing orientations can be adjusted for optimized coverage and redundancy, with different optimization approaches available to address this problem. Particularly, as different optimization parameters may be considered, the redundant coverage maximization issue may be treated as a multi-objective problem, with some potential solutions to be conside- red. In this context, two different evolutionary algorithms are proposed to compute redundant coverage maximization for target viewing, intending to be more efficient alternatives to greedy-based algorithms. Simulation results reinforce the benefits of employing evolutionary algorithms for adjustments of sensors? orientations, poten- tially benefiting deployment and management of wireless visual sensor networks for different applications. / As redes de sensores visuais sem fio podem obter, atrav?s de c?meras, informa??es importantes para aplica??es de controle e monitoramento, e tem ganhado aten??o da comunidade acad?mica nos ?ltimos anos. Para algumas aplica??es, um conjunto de alvos deve ser coberto por sensores visuais, e por vezes com demanda de redund?ncia de cobertura, especialmente quando h? requisitos de disponibilidade ou demandas de m?ltiplas perspectivas de cobertura para os alvos visados. Para sensores visuais rotacion?veis, as orienta??es de detec??o podem ser ajustadas para otimizar cobertura e redund?ncia, existindo diferentes abordagens de otimiza??o dispon?veis para solucionar esse problema. Particularmente, como diferentes par?metros de otimizac?o podem ser considerados, o problema de maximiza??o de cobertura redundante pode ser tratado como um problema multiobjetivo, com algumas solu??es potenciais a serem consideradas. Neste contexto, dois algoritmos evolutivos diferentes s?o propostos para calcular a maximiza??o de cobertura redundante para visualiza??o de alvos, pretendendo ser alternativas mais eficientes para algoritmos gulosos. Os resultados da simula??o refor?am os benef?cios de empregar algoritmos evolutivos para ajustes das orienta??es dos sensores, potencialmente beneficiando a implanta??o e o gerenciamento de redes de sensores visuais sem fio para diferentes aplica??es.