Integrated camera systems for increasing safety and maneuverability are becoming increasingly common for heavy vehicles. One problem with heavy vehicles today is that there are blind spots where the driver has no or very little view. There is a great demand on increasing the safety and helping the driver to get a better view of his surroundings. This can be achieved by a sophisticated camera system, using cameras with wide field of view, that could cover dangerous blind spots. This master thesis aims to investigate and develop a prototype solution for a camera system consisting of two fisheye cameras. The solution covers both hardware choices and software development including camera calibration and image stitching. Two different fisheye camera calibration toolboxes are compared and their results discussed, with the aim to find the most suitable for this application. The result from the two toolboxes differ in performance, and the result from only one of the toolboxes is sufficient for image stitching.

La perception de scènes routières est un domaine de recherche très actif. Cette thèse se focalise sur la détection et le suivi d’objets par fusion de données d’un système multi-capteurs composé d’un télémètre laser, une caméra fisheye et un système de positionnement global (GPS). Plusieurs étapes de la chaîne de perception sont ´ étudiées : le calibrage extrinsèque du couple caméra fisheye / télémètre laser, la détection de la route et enfin la détection et le suivi d’obstacles sur la route.Afin de traiter les informations géométriques du télémètre laser et de la caméra fisheye dans un repère commun, une nouvelle approche de calibrage extrinsèque entre les deux capteurs est proposée. La caméra fisheye est d’abord calibrée intrinsèquement. Pour cela, trois modèles de la littérature sont étudiés et comparés. Ensuite, pour le calibrage extrinsèque entre les capteurs,la normale au plan du télémètre laser est estimée par une approche de RANSAC couplée `a une régression linéaire `a partir de points connus dans le repère des deux capteurs. Enfin une méthode des moindres carres basée sur des contraintes géométriques entre les points connus, la normale au plan et les données du télémètre laser permet de calculer les paramètres extrinsèques. La méthode proposée est testée et évaluée en simulation et sur des données réelles.On s’intéresse ensuite `a la détection de la route à partir des données issues de la caméra fisheye et du télémètre laser. La détection de la route est initialisée `a partir du calcul de l’image invariante aux conditions d’illumination basée sur l’espace log-chromatique. Un seuillage sur l’histogramme normalisé est appliqué pour classifier les pixels de la route. Ensuite, la cohérence de la détection de la route est vérifiée en utilisant les mesures du télémètre laser. La segmentation de la route est enfin affinée en exploitant deux détections de la route successives. Pour cela, une carte de distance est calculée dans l’espace couleur HSI (Hue,Saturation, Intensity). La méthode est expérimentée sur des données réelles. Une méthode de détection d’obstacles basée sur les données de la caméra fisheye, du télémètre laser, d’un GPS et d’une cartographie routière est ensuite proposée. On s’intéresse notamment aux objets mobiles apparaissant flous dans l’image fisheye. Les régions d’intérêts de l’image sont extraites `a partir de la méthode de détection de la route proposée précédemment. Puis, la détection dans l’image du marquage de la ligne centrale de la route est mise en correspondance avec un modelé de route reconstruit `a partir des données GPS et cartographiques. Pour cela, la transformation IPM (Inverse Perspective Mapping) est appliquée à l’image. Les régions contenant potentiellement des obstacles sont alors extraites puis confirmées à l’aide du télémètre laser.L’approche est testée sur des données réelles et comparée `a deux méthodes de la littérature. Enfin, la dernière problématique étudiée est le suivi temporel des obstacles détectés `a l’aide de l’utilisation conjointe des données de la caméra fisheye et du télémètre laser. Pour cela, les resultats de détection d’obstacles précédemment obtenus sont exploit ´es ainsi qu’une approche de croissance de région. La méthode proposée est également testée sur des données réelles. / Road scene understanding is one of key research topics of intelligent vehicles. This thesis focuses on detection and tracking of obstacles by multisensors data fusion and analysis. The considered system is composed of a lidar, a fisheye camera and aglobal positioning system (GPS). Several steps of the perception scheme are studied: extrinsic calibration between fisheye camera and lidar, road detection and obstacles detection and tracking. Firstly, a new method for extinsic calibration between fisheye camera and lidar is proposed. For intrinsic modeling of the fisheye camera, three models of the literatureare studied and compared. For extrinsic calibration between the two sensors, the normal to the lidar plane is firstly estimated based on the determination of ń known ż points. The extrinsic parameters are then computed using a least square approachbased on geometrical constraints, the lidar plane normal and the lidar measurements. The second part of this thesis is dedicated to road detection exploiting both fisheye camera and lidar data. The road is firstly coarse detected considering the illumination invariant image. Then the normalised histogram based classification is validated using the lidar data. The road segmentation is finally refined exploiting two successive roaddetection results and distance map computed in HSI color space. The third step focuses on obstacles detection, especially in case of motion blur. The proposed method combines previously detected road, map, GPS and lidar information.Regions of interest are extracted from previously road detection. Then road central lines are extracted from the image and matched with road shape model extracted from 2DŋSIG map. Lidar measurements are used to validated the results.The final step is object tracking still using fisheye camera and lidar. The proposed method is based on previously detected obstacles and a region growth approach. All the methods proposed in this thesis are tested, evaluated and compared to stateŋofŋtheŋart approaches using real data acquired with the IRTESŋSET laboratory experimental platform.

Provision of live streaming of video from fisheye camera is a popular business in the IT sector. Video dewarping is one of its special fields that expands rapidly. As the requirement of video quality becomes higher and higher, there is an increasing need for efficient solutions that can be utilized to process videos in attempts to gain desirable results. The problem is to determine the right combination of transmission bitrate and resolution for live streaming of the dewarped videos. The purpose of this thesis is to develop a prototype solution for dewarping video from fisheye camera and re-stream it to a client. This prototype is used for testing combinations of bitrate and resolution of the video in different scenarios. A system is devised to live stream a video from a fisheye camera, dewarp the video in a server and display the video in media players. The results reveal that the combination of bitrate 3.5 - 4.5 Mbps and resolution 720p is best suited for transmission to avoid noticeable lagging in playback. Comments of observers prove the promising use of the dewarped videos as Virtual Reality(VR) technology. / Direktsänd videoströmning från en kamera med fiskögaobjektiv är ett populärt och snabbväxande, speciellt inom vissa områden som videoförvrängning korrigering. Eftersom kravet på hög högkvalitativ video blir högre och högre, ökas också behovet av en effektiv videobearbetnings lösning för att få önskvärda resultat. Problemet är att bestämma rätt kombination av överföringsbithastighet och upplösning för direktströmning av bearbetade videon. Syftet med detta examensarbete är att utveckla en prototyplösning som korrigerar videoförvrängning från en kamera med fisköga-objektiv samt vidaresända den korrigerade videon till en klient. Denna prototyp används för att testa olika kombinationer av bithastighet och upplösning i olika scenarier. Ett prototypsystem utvecklades för att direktsända video från en kamera med fisköga-objektiv, korrigera videoförvrängningen i en server och spela upp de korrigerade video i en mediaspelare. Resultatet visar att kombinationen av bithastigheten mellan 3.5 - 4.5 Mbps och upplösningen 720p är den mest lämpliga för att undvika märkbara fördröjningar hos klienten. Den potentiella framtida användningen av den bearbetade videon inom Virtuell verklighet (VV) är lovande baserat på observatörernas kommentarer.

The Professorship of Digital Signal Processing and Circuit Technology of Chemnitz University of Technology conducts research in the field of three-dimensional space measurement with optical sensors. In recent years this field has made major progress. For example innovative, active techniques such as the “structured light“-principle are able to measure even homogeneous surfaces and find its way into the consumer electronic market in terms of Microsoft’s Kinect® at the present time. Furthermore, high-resolution optical sensors establish powerful, passive stereo vision systems in the field of indoor surveillance. Thereby they induce new application domains such as security and assistance systems for domestic environments. However, the constraint field of view can be still considered as an essential characteristic of all these technologies. For instance, in order to measure a volume in size of a living space, two to three deployed 3D sensors have to be applied nowadays. This is due to the fact that the commonly utilized perspective projection principle constrains the visible area to a field of view of approximately 120°. On the contrary, novel fish-eye lenses allow the realization of omnidirectional projection models. Therewith, the visible field of view can be enlarged up to more than 180°. In combination with a 3D measurement approach, thus, the number of required sensors for entire room coverage can be reduced considerably. Motivated by the requirements of the field of indoor surveillance, the present work focuses on the combination of the established stereo vision principle and omnidirectional projection methods. The entire 3D measurement of a living space by means of one single sensor can be considered as major objective. As a starting point for this thesis chapter 1 discusses the underlying requirement, referring to various relevant fields of application. Based on this, the distinct purpose for the present work is stated. The necessary mathematical foundations of computer vision are reflected in Chapter 2 subsequently. Based on the geometry of the optical imaging process, the projection characteristics of relevant principles are discussed and a generic method for modeling fish-eye cameras is selected. Chapter 3 deals with the extraction of depth information using classical (perceptively imaging) binocular stereo vision configurations. In addition to a complete recap of the processing chain, especially occurring measurement uncertainties are investigated. In the following, Chapter 4 addresses special methods to convert different projection models. The example of mapping an omnidirectional to a perspective projection is employed, in order to develop a method for accelerating this process and, hereby, for reducing the computational load associated therewith. Any errors that occur, as well as the necessary adjustment of image resolution, are an integral part of the investigation. As a practical example, an application for person tracking is utilized in order to demonstrate to which extend the usage of “virtual views“ can increase the recognition rate for people detectors in the context of omnidirectional monitoring. Subsequently, an extensive search with respect to omnidirectional imaging stereo vision techniques is conducted in chapter 5. It turns out that the complete 3D capture of a room is achievable by the generation of a hemispherical depth map. Therefore, three cameras have to be combined in order to form a trinocular stereo vision system. As a basis for further research, a known trinocular stereo vision method is selected. Furthermore, it is hypothesized that, applying a modified geometric constellation of cameras, more precisely in the form of an equilateral triangle, and using an alternative method to determine the depth map, the performance can be increased considerably. A novel method is presented, which shall require fewer operations to calculate the distance information and which is to avoid a computational costly step for depth map fusion as necessary in the comparative method. In order to evaluate the presented approach as well as the hypotheses, a hemispherical depth map is generated in Chapter 6 by means of the new method. Simulation results, based on artificially generated 3D space information and realistic system parameters, are presented and subjected to a subsequent error estimate. A demonstrator for generating real measurement information is introduced in Chapter 7. In addition, the methods that are applied for calibrating the system intrinsically as well as extrinsically are explained. It turns out that the calibration procedure utilized cannot estimate the extrinsic parameters sufficiently. Initial measurements present a hemispherical depth map and thus con.rm the operativeness of the concept, but also identify the drawbacks of the calibration used. The current implementation of the algorithm shows almost real-time behaviour. Finally, Chapter 8 summarizes the results obtained along the studies and discusses them in the context of comparable binocular and trinocular stereo vision approaches. For example the results of the simulations carried out produced a saving of up to 30% in terms of stereo correspondence operations in comparison with a referred trinocular method. Furthermore, the concept introduced allows the avoidance of a weighted averaging step for depth map fusion based on precision values that have to be calculated costly. The achievable accuracy is still comparable for both trinocular approaches. In summary, it can be stated that, in the context of the present thesis, a measurement system has been developed, which has great potential for future application fields in industry, security in public spaces as well as home environments.

The Professorship of Digital Signal Processing and Circuit Technology of Chemnitz University of Technology conducts research in the field of three-dimensional space measurement with optical sensors. In recent years this field has made major progress. For example innovative, active techniques such as the “structured light“-principle are able to measure even homogeneous surfaces and find its way into the consumer electronic market in terms of Microsoft’s Kinect® at the present time. Furthermore, high-resolution optical sensors establish powerful, passive stereo vision systems in the field of indoor surveillance. Thereby they induce new application domains such as security and assistance systems for domestic environments. However, the constraint field of view can be still considered as an essential characteristic of all these technologies. For instance, in order to measure a volume in size of a living space, two to three deployed 3D sensors have to be applied nowadays. This is due to the fact that the commonly utilized perspective projection principle constrains the visible area to a field of view of approximately 120°. On the contrary, novel fish-eye lenses allow the realization of omnidirectional projection models. Therewith, the visible field of view can be enlarged up to more than 180°. In combination with a 3D measurement approach, thus, the number of required sensors for entire room coverage can be reduced considerably. Motivated by the requirements of the field of indoor surveillance, the present work focuses on the combination of the established stereo vision principle and omnidirectional projection methods. The entire 3D measurement of a living space by means of one single sensor can be considered as major objective. As a starting point for this thesis chapter 1 discusses the underlying requirement, referring to various relevant fields of application. Based on this, the distinct purpose for the present work is stated. The necessary mathematical foundations of computer vision are reflected in Chapter 2 subsequently. Based on the geometry of the optical imaging process, the projection characteristics of relevant principles are discussed and a generic method for modeling fish-eye cameras is selected. Chapter 3 deals with the extraction of depth information using classical (perceptively imaging) binocular stereo vision configurations. In addition to a complete recap of the processing chain, especially occurring measurement uncertainties are investigated. In the following, Chapter 4 addresses special methods to convert different projection models. The example of mapping an omnidirectional to a perspective projection is employed, in order to develop a method for accelerating this process and, hereby, for reducing the computational load associated therewith. Any errors that occur, as well as the necessary adjustment of image resolution, are an integral part of the investigation. As a practical example, an application for person tracking is utilized in order to demonstrate to which extend the usage of “virtual views“ can increase the recognition rate for people detectors in the context of omnidirectional monitoring. Subsequently, an extensive search with respect to omnidirectional imaging stereo vision techniques is conducted in chapter 5. It turns out that the complete 3D capture of a room is achievable by the generation of a hemispherical depth map. Therefore, three cameras have to be combined in order to form a trinocular stereo vision system. As a basis for further research, a known trinocular stereo vision method is selected. Furthermore, it is hypothesized that, applying a modified geometric constellation of cameras, more precisely in the form of an equilateral triangle, and using an alternative method to determine the depth map, the performance can be increased considerably. A novel method is presented, which shall require fewer operations to calculate the distance information and which is to avoid a computational costly step for depth map fusion as necessary in the comparative method. In order to evaluate the presented approach as well as the hypotheses, a hemispherical depth map is generated in Chapter 6 by means of the new method. Simulation results, based on artificially generated 3D space information and realistic system parameters, are presented and subjected to a subsequent error estimate. A demonstrator for generating real measurement information is introduced in Chapter 7. In addition, the methods that are applied for calibrating the system intrinsically as well as extrinsically are explained. It turns out that the calibration procedure utilized cannot estimate the extrinsic parameters sufficiently. Initial measurements present a hemispherical depth map and thus con.rm the operativeness of the concept, but also identify the drawbacks of the calibration used. The current implementation of the algorithm shows almost real-time behaviour. Finally, Chapter 8 summarizes the results obtained along the studies and discusses them in the context of comparable binocular and trinocular stereo vision approaches. For example the results of the simulations carried out produced a saving of up to 30% in terms of stereo correspondence operations in comparison with a referred trinocular method. Furthermore, the concept introduced allows the avoidance of a weighted averaging step for depth map fusion based on precision values that have to be calculated costly. The achievable accuracy is still comparable for both trinocular approaches. In summary, it can be stated that, in the context of the present thesis, a measurement system has been developed, which has great potential for future application fields in industry, security in public spaces as well as home environments.:Abstract 7 Zusammenfassung 11 Acronyms 27 Symbols 29 Acknowledgement 33 1 Introduction 35 1.1 Visual Surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.2 Challenges in Visual Surveillance . . . . . . . . . . . . . . . . . . . . . . . 38 1.3 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2 Fundamentals of Computer Vision Geometry 43 2.1 Projective Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.1.1 Euclidean Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.1.2 Projective Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.2 Camera Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.2.1 Geometrical Imaging Process . . . . . . . . . . . . . . . . . . . . . 45 2.2.1.1 Projection Models . . . . . . . . . . . . . . . . . . . . . . 46 2.2.1.2 Intrinsic Model . . . . . . . . . . . . . . . . . . . . . . . . 47 2.2.1.3 Extrinsic Model . . . . . . . . . . . . . . . . . . . . . . . 50 2.2.1.4 Distortion Models . . . . . . . . . . . . . . . . . . . . . . 51 2.2.2 Pinhole Camera Model . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.2.2.1 Complete Forward Model . . . . . . . . . . . . . . . . . . 52 2.2.2.2 Back Projection . . . . . . . . . . . . . . . . . . . . . . . 53 2.2.3 Equiangular Camera Model . . . . . . . . . . . . . . . . . . . . . . 54 2.2.4 Generic Camera Models . . . . . . . . . . . . . . . . . . . . . . . . 55 2.2.4.1 Complete Forward Model . . . . . . . . . . . . . . . . . . 56 2.2.4.2 Back Projection . . . . . . . . . . . . . . . . . . . . . . . 58 2.3 Camera Calibration Methods . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.3.1 Perspective Camera Calibration . . . . . . . . . . . . . . . . . . . . 59 2.3.2 Omnidirectional Camera Calibration . . . . . . . . . . . . . . . . . 59 2.4 Two-View Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 2.4.1 Epipolar Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 2.4.2 The Fundamental Matrix . . . . . . . . . . . . . . . . . . . . . . . 63 2.4.3 Epipolar Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3 Fundamentals of Stereo Vision 67 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.1.1 The Concept Stereo Vision . . . . . . . . . . . . . . . . . . . . . . 67 3.1.2 Overview of a Stereo Vision Processing Chain . . . . . . . . . . . . 68 3.2 Stereo Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 3.2.1 Extrinsic Stereo Calibration With Respect to the Projective Error 70 3.3 Stereo Rectification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.3.1 A Compact Algorithm for Rectification of Stereo Pairs . . . . . . . 73 3.4 Stereo Correspondence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 3.4.1 Disparity Computation . . . . . . . . . . . . . . . . . . . . . . . . 76 3.4.2 The Correspondence Problem . . . . . . . . . . . . . . . . . . . . . 77 3.5 Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 3.5.1 Depth Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . 79 3.5.2 Range Field of Measurement . . . . . . . . . . . . . . . . . . . . . 80 3.5.3 Measurement Accuracy . . . . . . . . . . . . . . . . . . . . . . . . 80 3.5.4 Measurement Errors . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.5.4.1 Quantization Error . . . . . . . . . . . . . . . . . . . . . 82 3.5.4.2 Statistical Distribution of Quantization Errors . . . . . . 83 4 Virtual Cameras 87 4.1 Introduction and Related Works . . . . . . . . . . . . . . . . . . . . . . . 88 4.2 Omni to Perspective Vision . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.1 Forward Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.2 Backward Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.2.3 Fast Backward Mapping . . . . . . . . . . . . . . . . . . . . . . . . 96 4.3 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.4 Accuracy Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.4.1 Intrinsics of the Source Camera . . . . . . . . . . . . . . . . . . . . 102 4.4.2 Intrinsics of the Target Camera . . . . . . . . . . . . . . . . . . . . 102 4.4.3 Marginal Virtual Pixel Size . . . . . . . . . . . . . . . . . . . . . . 104 4.5 Performance Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.6 Virtual Perspective Views for Real-Time People Detection . . . . . . . . . 110 5 Omnidirectional Stereo Vision 113 5.1 Introduction and Related Works . . . . . . . . . . . . . . . . . . . . . . . 113 5.1.1 Geometrical Configuration . . . . . . . . . . . . . . . . . . . . . . . 116 5.1.1.1 H-Binocular Omni-Stereo with Panoramic Views . . . . . 117 5.1.1.2 V-Binocular Omnistereo with Panoramic Views . . . . . 119 5.1.1.3 Binocular Omnistereo with Hemispherical Views . . . . . 120 5.1.1.4 Trinocular Omnistereo . . . . . . . . . . . . . . . . . . . 122 5.1.1.5 Miscellaneous Configurations . . . . . . . . . . . . . . . . 125 5.2 Epipolar Rectification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 5.2.1 Cylindrical Rectification . . . . . . . . . . . . . . . . . . . . . . . . 127 5.2.2 Epipolar Equi-Distance Rectification . . . . . . . . . . . . . . . . . 128 5.2.3 Epipolar Stereographic Rectification . . . . . . . . . . . . . . . . . 128 5.2.4 Comparison of Rectification Methods . . . . . . . . . . . . . . . . 129 5.3 A Novel Spherical Stereo Vision Setup . . . . . . . . . . . . . . . . . . . . 129 5.3.1 Physical Omnidirectional Camera Configuration . . . . . . . . . . 131 5.3.2 Virtual Rectified Cameras . . . . . . . . . . . . . . . . . . . . . . . 131 6 A Novel Spherical Stereo Vision Algorithm 135 6.1 Matlab Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . 135 6.2 Extrinsic Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6.3 Physical Camera Configuration . . . . . . . . . . . . . . . . . . . . . . . . 137 6.4 Virtual Camera Configuration . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.4.1 The Focal Length . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 6.4.2 Prediscussion of the Field of View . . . . . . . . . . . . . . . . . . 138 6.4.3 Marginal Virtual Pixel Sizes . . . . . . . . . . . . . . . . . . . . . . 139 6.4.4 Calculation of the Field of View . . . . . . . . . . . . . . . . . . . 142 6.4.5 Calculation of the Virtual Pixel Size Ratios . . . . . . . . . . . . . 143 6.4.6 Results of the Virtual Camera Parameters . . . . . . . . . . . . . . 144 6.5 Spherical Depth Map Generation . . . . . . . . . . . . . . . . . . . . . . . 147 6.5.1 Omnidirectional Imaging Process . . . . . . . . . . . . . . . . . . . 148 6.5.2 Rectification Process . . . . . . . . . . . . . . . . . . . . . . . . . . 148 6.5.3 Rectified Depth Map Generation . . . . . . . . . . . . . . . . . . . 150 6.5.4 Spherical Depth Map Generation . . . . . . . . . . . . . . . . . . . 151 6.5.5 3D Reprojection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 6.6 Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 7 Stereo Vision Demonstrator 163 7.1 Physical System Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 7.2 System Calibration Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 165 7.2.1 Intrinsic Calibration of the Physical Cameras . . . . . . . . . . . . 165 7.2.2 Extrinsic Calibration of the Physical and the Virtual Cameras . . 166 7.2.2.1 Extrinsic Initialization of the Physical Cameras . . . . . 167 7.2.2.2 Extrinsic Initialization of the Virtual Cameras . . . . . . 167 7.2.2.3 Two-View Stereo Calibration and Rectification . . . . . . 167 7.2.2.4 Three-View Stereo Rectification . . . . . . . . . . . . . . 168 7.2.2.5 Extrinsic Calibration Results . . . . . . . . . . . . . . . . 169 7.3 Virtual Camera Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 7.4 Software Realization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 7.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 7.5.1 Qualitative Assessment . . . . . . . . . . . . . . . . . . . . . . . . 172 7.5.2 Performance Measurements . . . . . . . . . . . . . . . . . . . . . . 174 8 Discussion and Outlook 177 8.1 Discussion of the Current Results and Further Need for Research . . . . . 177 8.1.1 Assessment of the Geometrical Camera Configuration . . . . . . . 178 8.1.2 Assessment of the Depth Map Computation . . . . . . . . . . . . . 179 8.1.3 Assessment of the Depth Measurement Error . . . . . . . . . . . . 182 8.1.4 Assessment of the Spherical Stereo Vision Demonstrator . . . . . . 183 8.2 Review of the Different Approaches for Hemispherical Depth Map Generation184 8.2.1 Comparison of the Equilateral and the Right-Angled Three-View Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 8.2.2 Review of the Three-View Approach in Comparison with the Two- View Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 8.3 A Sample Algorithm for Human Behaviour Analysis . . . . . . . . . . . . 187 8.4 Closing Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 A Relevant Mathematics 191 A.1 Cross Product by Skew Symmetric Matrix . . . . . . . . . . . . . . . . . . 191 A.2 Derivation of the Quantization Error . . . . . . . . . . . . . . . . . . . . . 191 A.3 Derivation of the Statistical Distribution of Quantization Errors . . . . . . 192 A.4 Approximation of the Quantization Error for Equiangular Geometry . . . 194 B Further Relevant Publications 197 B.1 H-Binocular Omnidirectional Stereo Vision with Panoramic Views . . . . 197 B.2 V-Binocular Omnidirectional Stereo Vision with Panoramic Views . . . . 198 B.3 Binocular Omnidirectional Stereo Vision with Hemispherical Views . . . . 200 B.4 Trinocular Omnidirectional Stereo Vision . . . . . . . . . . . . . . . . . . 201 B.5 Miscellaneous Configurations . . . . . . . . . . . . . . . . . . . . . . . . . 202 Bibliography 209 List of Figures 223 List of Tables 229 Affidavit 231 Theses 233 Thesen 235 Curriculum Vitae 237