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The last decades has seen the rise of digitization and automation of certain tasks at dangerous work environments though this has not yet affected the firefighting profession. With sensors and remote vehicles it is possible to aid the firefighters in analyzing a situation and aid them in the extinguishing efforts which reduces risks and time spent finding the fires. This thesis proposes a solution to such a problem by designing and implementing a case study. The test system was tested in a controlled environment to verify the functionality of the system. The results show that this is possible using low resolution thermal imaging equipment. The case study also shows that standard imaging equipment is a useful tool to determine the severity of the scene as well as searching for people. / Under de senaste decennierna har digitaliseringen och automatisering av uppgifter i farliga arbetsmiljöer ökat, detta har inte ännu påverkat brandkåren. Med hjälp av sensorer och fjärrstyrda fordon kan detta göra det möjligt att hjälpa brandmän att analysera en situation och hjälpa till med brandsläckning vilket reducerar riskerna och tid att hitta bränder. Denna avhandling framför en lösning till ett sådant problem genom att skapa och implementera en fallstudie. Testsystemet var testat i en kontrollerad miljö att verifiera funktionaliteten av systemet. Resultaten visar att det är möjligt att använda sig av en lågupplöst värmekamera. Fallstudien visade även att bildutrustning är ett användbart verktyg för att bestämma allvarlighetsgraden av miljön och för att söka efter människor.

The purpose of information acquisition for data and information fusion is to provide relevant and timely information. The acquired information is integrated (or fused) to estimate the state of some environment. The success of information acquisition can be measured in the quality of the environment state estimates generated by the data and information fusion process. In this thesis, we introduce and set out to characterise the concept of large-scale information acquisition. Our interest in this subject is justified both by the identified lack of research on a holistic view on data and information fusion, and the proliferation of networked sensors which promises to enable handy access to a multitude of information sources. We identify a number of properties that could be considered in the context of large-scale information acquisition. The sensors used could be large in number, heterogeneous, complex, and distributed. Also, algorithms for large-scale information acquisition may have to deal with decentralised control and multiple and varying objectives. In the literature, a process that realises information acquisition is frequently denoted sensor management. We, however, introduce the term perception management instead, which encourages an agent perspective on information acquisition. Apart from explictly inviting the wealth of agent theory research into the data and information fusion research, it also highlights that the resource usage of perception management is constrained by the overall control of a system that uses data and information fusion. To address the challenges posed by the concept of large-scale information acquisition, we present a framework which highlights some of its pertinent aspects. We have implemented some important parts of the framework. What becomes evident in our study is the innate complexity of information acquisition for data and information fusion, which suggests approximative solutions. We, furthermore, study one of the possibly most important properties of large-scale information acquisition, decentralised control, in more detail. We propose a recurrent negotiation protocol for (decentralised) multi-agent coordination. Our approach to the negotiations is from an axiomatic bargaining theory perspective; an economics discipline. We identify shortcomings of the most commonly applied bargaining solution and demonstrate in simulations a problem instance where it is inferior to an alternative solution. However, we can not conclude that one of the solutions dominates the other in general. They are both preferable in different situations. We have also implemented the recurrent negotiation protocol on a group of mobile robots. We note some subtle difficulties with transferring bargaining solutions from economics to our computational problem. For instance, the characterising axioms of solutions in bargaining theory are useful to qualitatively compare different solutions, but care has to be taken when translating the solution to algorithms in computer science as some properties might be undesirable, unimportant or risk being lost in the translation. / QC 20100903

I flera års tid har antalet svåra och okontrollerade skogsbränder ökat i antal. Det finns ett behov av att detektera skogsbränder med hjälp av satelliter som har högre tidsupplösning samt högre geometrisk upplösning än de satelliter som är i bruk idag. I detta examensarbete utfördes dels en studie av bildframställningen hos, och dels designen av omloppsbanan till en mikrosatellitkonstellation, som använder sig av nära-infraröd (NIR) och kortvågig infraröd (SWIR) avbildningsteknik med förstärkt geometrisk upplösning och tidsupplösning för övervakning av skogsbränder. En översikt av den nuvarande tekniken visade olika alternativ för kamera: två kommersiella sensorer med ett spektralområde på 0.7 till 1.7 μm och geometrisk upplösning på 140 m respektive 112 m; en kommersiell sensor med ett spektralområde på upp till 2.2 μm och geometrisk upplösning på 168 m; samt ett förslag till sensor med högre geometrisk upplösning på 20 m eller 50 m, som erhålls genom att öka brännvidden. Flera slutsatser nåddes med hänsyn till avbildningen: lämpligheten hos linserna som hittades till respektive sensor verifierades, och det upptäcktes att förvrängningen till följd av jordens rotation ökade med förlängd exponeringstid, vilket även skedde för förhållandet mellan signal och störningar. Ett förslag till en cirkulär, solsynkron, polär omloppsbana med ett dagligen upprepande mönster lades fram. En förenklad metod för beräkning av halva storaxeln tillämpades, och därmed bestämdes en höjd på 561 km och lutning på 97.64°. I enlighet med detta uppskattades antalet satelliter som krävs för både global och regional (Sverige) täckning för alla avbildningsalternativ. För global täckning beräknades antalet satelliter som krävs för att uppnå en geometrisk upplösning på 140.25 m till 15 stycken, medan en geometrisk upplösning på 50 m krävde 84 stycken satelliter. För regional täckning (Sverige) krävdes å andra sidan endast 6 stycken satelliter för att uppnå en geometrisk upplösning på 140.25 m, och 32 stycken för en geometrisk upplösning på 50 m. Detta examensarbete har visat hur många satelliter som behövs för antingen global eller regional täckning så att skogsbränder kan detekteras med högre geometrisk upplösning samt högre tidsupplösning. / For several years, the occurrence of more severe and uncontrolled wildfires has been increasing. There is a need to detect wildfires with a higher spatial and temporal resolution than the ones currently provided by operational satellites. In this thesis, an imager study and orbital design of a microsatellite constellation using near infrared (NIR) and shortwave infrared (SWIR) imagers for wildfire monitoring, with enhanced spatial and temporal resolution, was conducted. After a state of the art review, different alternatives of imaging systems were discerned: two commercial sensors with spectral range of 0.7 to 1.7 μm and spatial resolution of 140 m and 112 m, respectively; one commercial sensor with spectral range up to 2.2 μm and spatial resolution of 168 m; and one sensor proposal with higher spatial resolution of 20 m or 50 m, achieved by increasing the focal length. Several conclusions were reached with regards to the imagers: the appropriateness of lenses found for each sensor was confirmed, the Earth rotation distortion was found to increase as the exposure time is extended, as did the signal-to noise ratio. A proposal for a circular sun-synchronous polar orbit with a daily repeating pattern was made. Applying a simplified method to calculate the semi-major axis, an altitude of 561 km and inclination of 97.64° were determined. Accordingly, the number of satellites for both global and regional (Sweden) coverage was estimated for all imager alternatives. For global coverage, the necessary number of satellites to achieve a spatial resolution of 140.25 m was calculated to be 15 satellites, whereas for a spatial resolution of 50 m the number of satellites increased to 84. On the other hand, for regional coverage (Sweden), the number of satellites to achieve a spatial resolution of 140.25 m were 6, and for a spatial resolution of 50 m the number of satellites was 32. This thesis shows how many satellites are required for either global or regional coverage, considering different imager configurations, to detect wildfires with a higher spatial and temporal resolution.

L’objectif du travail de cette thèse est la contribution au développement de nouvelles techniques dans le domaine dessystèmes de détection sans marqueur pour une utilisation dans trois domaines vitaux de la santé en utilisant des capteursinnovants et peu coûteux. Pour la réalisation de nos objectifs nous avons eu recours principalement à de l’électroniqueembarquées et du traitement du signal en utilisant le capteur Kinect. Des résultats encourageants ont été obtenus et sontprésentés tout au long de cette thèse. Dans la première partie de ce travail, nous présentons un nouveau système desurveillance visuelle sans marqueur en temps réel pour détecter et suivre les personnes âgées et surveiller leurs activitésdans leur environnement intérieur en utilisant un réseau de capteurs Kinect. Le système identifie également l’événementde chute des personnes âgées sous surveillance. Dans la deuxième partie nous utilisons également le capteur Kinectmais cette fois ci pour la détection sans marqueur des mouvements de la tête d’un patient lors d’un examen utilisant LaTomographie par Emission de Positons (CT/PET) du cerveau. Ce travail est basé sur la compensation de la dégradationde l’image TEP due aux mouvements de la tête du patient. Pour nos essais un cobaye dit « fantôme » a été réalisé,les résultats sur le fantôme sont prometteur ce qui a donné lieu à un test sur un vrai patient volontaire. Les résultatsfinaux montrent l’efficacité de ce nouveau système. La troisième partie du travail présente la mise en oeuvre d’un nouveausystème intelligent pour contrôler un fauteuil roulant électrique par des mouvements spéciaux de la tête toujours sansmarqueur. Un algorithme adapté est conçu pour détecter en continu les degrés des mouvements du visage en utilisant lecapteur Kinect. Fautes de fauteuil roulant électrique, le système a été testé sur un véhicule radio commandé. / The objective of the work of this thesis is the contribution in developing novel technical methods in the field of marker-lesssensing systems for use in three vital health areas by using new inexpensive sensors. Several scientific areas are involvedin achieving our objective such as; electronics and signal processing by using the Kinect sensor. Encouraging results wereachieved as presented throughout this thesis. In the first part of this work we present a new real-time marker-less visualsurveillance system for detecting and tracking seniors and monitoring their activities in the indoor environment by usingnetwork of Kinect sensors. The system also identifies the fall event with the elderly. In the second part, we present anew approach for a marker-less movement detection system for influential head movements in the brain Positron EmissionTomography imaging (CT/PET) by employing the Kinect sensor. This work addresses the compensation of the PET imagedegradation due to subject’s head movements. A developed particular phantom and volunteer studies were carried out.The experimental results show the effectiveness of this new system. The third part of the work presents the design andimplementation of a new smart system for controlling an electric wheelchair by special mark-less head movements. Anadaptable algorithm is designed to continuously detect the rotation degrees of the face pose using the Kinect sensor inreal-time that are interpreted as controlling signals through a hardware interface for the electric wheelchair actuators.

This thesis presents the development of a Sensor Tower Platform (STP) comprised of an array of Passive Infra-Red (PIR) sensors along with a classification algorithm that enables the STP to distinguish between human intrusion, animal intrusion and clutter arising from wind-blown vegetative movement in an outdoor environment. The research was motivated by the aim of exploring the potential use of wireless sensor networks (WSNs) as an early-warning system to help mitigate human-wildlife conflicts occurring at the edge of a forest. While PIR sensors are in commonplace use in indoor settings, their use in an outdoor environment is hampered by the fact that they are prone to false alarms arising from wind-blown vegetation. Every PIR sensor is made up of one or more pairs of pyroelectric pixels arranged in a plane, and the orientation of interest in this thesis is one in which this plane is a vertical plane, i.e., a plane perpendicular to the ground plane. The intersection of the Field Of View (FOV) of the PIR sensor with a second vertical plane that lies within the FOV of the PIR sensor, is called the virtual pixel array (VPA). The structure of the VPA corresponding to the plane along which intruder motion takes place determines the form of the signal generated by the PIR sensor. The STP developed in this thesis employs an array of PIR sensors designed so as to result in a VPA that makes it easier to discriminate between human and animal intrusion while keeping to a small level false alarms arising from vegetative motion. The design was carried out in iterative fashion, with each successive iteration separated by a lengthy testing phase. There were a total of 5 design iterations spanning a total period of 14 months. Given the inherent challenges involved in gathering data corresponding to animal intrusion, the testing of the SP was carried out both using real-world data and through simulation. Simulation was carried out by developing a tool that employed animation software to simulate intruder and animal motion as well as some limited models of wind-blown vegetation. More specifically, the simulation tool employed 3-dimensional models of intruder and shrub motion that were developed using the popular animation software Blender. The simulated output signal of the PIR sensor was then generated by calculating the area of the 3-dimensional intruder when projected onto the VPA of the STP. An algorithm for efficiently calculating this to a good degree of approximation was implemented in Open Graphics Library (OpenGL). The simulation tool was useful both for evaluating various competing design alternatives as well as for developing an intuition for the kind of signals the SP would generate without the need for time-consuming and challenging animal-motion data collection. Real-world data corresponding to human motion was gathered on the campus of the Indian Institute of Science (IISc), while animal data was recorded at a dog-trainer facility in Kengeri as well as the Bannerghatta Biological Park, both located in the outskirts of Bengaluru. The array of PIR sensors was designed so as to result in a VPA that had good spatial resolution. The spatial resolution capabilities of the STP permitted distinguishing between human and animal motion with good accuracy based on low-complexity, signal-energy computations. Rejecting false alarms arising from vegetative movement proved to be more challenging. While the inherent spatial resolution of the STP was very helpful, an alternative approach turned out to have much higher accuracy, although it is computationally more intensive. Under this approach, the intruder signal, either human or animal, was modelled as a chirp waveform. When the intruder moves along a circular arc surrounding the STP, the resulting signal is periodic with constant frequency. However, when the intruder moves along a more likely straight-line path, the resultant signal has a strong chirp component. Clutter signals arising from vegetative motion does not exhibit this chirp behavior and an algorithm that exploited this difference turned in a classification accuracy in excess of 97%.

Les systèmes multicaméras utilisés pour la vidéosurveillance sont complexes, lourds et coûteux. Pour la surveillance d'une pièce, serait-il possible de les remplacer par un système beaucoup plus simple utilisant une seule caméra et une ou plusieurs sources lumineuses en misant sur les ombres projetées pour obtenir de l'information 3D ? Malgré les résultats intéressants offerts par les systèmes multicaméras, la quantité d'information à traiter et leur complexité limitent grandement leur usage. Dans le même contexte, nous proposons de simplifier ces systèmes en remplaçant une caméra par une source lumineuse. En effet, une source lumineuse peut être vue comme une caméra qui génère une image d'ombre révélant l'objet qui bloque la lumière. Notre système sera composé par une seule caméra et une ou plusieurs sources lumineuses infrarouges (invisibles à l'oeil). Malgré les difficultés prévues quant à l'extraction de l'ombre et la déformation et l'occultation de l'ombre par des obstacles (murs, meubles...), les gains sont multiples en utilisant notre système. En effet, on peut éviter ainsi les problèmes de synchronisation et de calibrage de caméras et réduire le coût en remplaçant des caméras par de simples sources infrarouges. Nous proposons deux approches différentes pour automatiser la reconnaissance de postures humaines. La première approche reconstruit la forme 3D d'une personne pour faire la reconnaissance de la posture en utilisant des descripteurs de forme. La deuxième approche combine directement l'information 2D (ombre+silhouette) pour faire la reconnaissance de postures. Scientifiquement, nous cherchons à prouver que l'information offerte par une silhouette et l'ombre générée par une source lumineuse est suffisante pour permettre la reconnaissance de postures humaines élémentaires (p.ex. debout, assise, couchée, penchée, etc.). Le système proposé peut être utilisé pour la vidéosurveillance d'endroits non encombrés tels qu'un corridor dans une résidence de personnes âgées (pour la détection des chutes p. ex.) ou d'une compagnie (pour la sécurité). Son faible coût permettrait un plus grand usage de la vidéosurveillance au bénéfice de la société. Au niveau scientifique, la démonstration théorique et pratique d'un tel système est originale et offre un grand potentiel pour la vidéosurveillance. / Human posture recognition (HPR) from video sequences is one of the major active research areas of computer vision. It is one step of the global process of human activity recognition (HAR) for behaviors analysis. Many HPR application systems have been developed including video surveillance, human-machine interaction, and the video retrieval. Generally, applications related to HPR can be achieved using mainly two approaches : single camera or multi-cameras. Despite the interesting performance achieved by multi-camera systems, their complexity and the huge information to be processed greatly limit their widespread use for HPR. The main goal of this thesis is to simplify the multi-camera system by replacing a camera by a light source. In fact, a light source can be seen as a virtual camera, which generates a cast shadow image representing the silhouette of the person that blocks the light. Our system will consist of a single camera and one or more infrared light sources. Despite some technical difficulties in cast shadow segmentation and cast shadow deformation because of walls and furniture, different advantages can be achieved by using our system. Indeed, we can avoid the synchronization and calibration problems of multiple cameras, reducing the cost of the system and the amount of processed data by replacing a camera by one light source. We introduce two different approaches in order to automatically recognize human postures. The first approach directly combines the person’s silhouette and cast shadow information, and uses 2D silhouette descriptor in order to extract discriminative features useful for HPR. The second approach is inspired from the shape from silhouette technique to reconstruct the visual hull of the posture using a set of cast shadow silhouettes, and extract informative features through 3D shape descriptor. Using these approaches, our goal is to prove the utility of the combination of person’s silhouette and cast shadow information for recognizing elementary human postures (stand, bend, crouch, fall,...) The proposed system can be used for video surveillance of uncluttered areas such as a corridor in a senior’s residence (for example, for the detection of falls) or in a company (for security). Its low cost may allow greater use of video surveillance for the benefit of society.

Rubber granulates, used at artificial grass pitches, pose a threat to the environment when leaking into the nature. As the granulates leak to the environment through rain water and snow clearances, they can be transported by rivers and later on end up in the marine life. Therefore, reducing the snow clearances to its minimum is of importance. If the snow clearance problem is minimized or even eliminated, this will have a positive impact on the surrounding nature. The object of this project is to propose a method for deciding when to remove snow and automate the information dispersing upon clearing or closing a pitch. This includes finding low powered sensors to measure snow depth, find a machine learning model to predict upcoming snow levels and create an application with a clear and easy-to-use interface to present weather information and disperse information to the responsible persons. Controlled experiments is used to find the models and sensors that are suitable to solve this problem. The sensors are tested on a single snow quality, where ultrasonic and infrared sensors are found suitable. However, fabricated tests for newly fallen snow questioned the possibility of measuring snow depth using the ultrasonic sensor in the general case. Random Forest is presented as the machine learning model that predicts future snow levels with the highest accuracy. From a survey, indications is found that the web application fulfills the intended functionalities, with some improvements suggested.