Match moving är ett av de steg inom en vfx produktion som avgör och skapar intergrerandet mellan filmat material och filmiska effekter gjorda i 3D.  Redan under en inspelning förekommer förberedande arbete och dokumentation för att i ett senare stadie underlätta för match moving. Detta för att utesluta specifika problem som kan uppstå under match moving processen då det är väsentligt att skapandet av en virtuell kamera är korrekt i förhållande till det filmade materialet. Detta för att vfx ska kunna adderas senare. Detta arbete tar upp den problematik som kan uppstå när förberedelser och dokumentation saknas för det filmade materialet. Arbetet fokuserar på tester av olika arbetsmetoder för match moving samt analyser av det filmade materialet. Detta för att klargöra hur en vfx artist ska gå tillväga för att göra valet av arbetsmetod beroende på hur det filmade materialet ser ut. De arbetsmetoderna som tas upp i detta arbete är automatic tracking, tracking med enbart 2D feature points samt planar tracking och dess förberedande steg. Resultaten visar även på att olika arbetsmetoder ska behandlas på olika sätt av vfx artisten samt att de kan ha olika resultat beroende på hur det filmade materialet ser ut. Klargörandet av dessa arbetsmetoder och det filmade materialet med hjälp av tester gör valet av arbetsmetod simplare när det kommer till match moving. / Match moving is one of the steps within a vfx production pipeline that decides how filmed material and 3D effects will intergrate with each other. Already on set preparations and documentations are made to make the match moving process less problematic later in the pipeline. It is essential that the filmed material match the virtual created camera from the match moving process to make the intergration happen. This report discusses the problems that can arise when needed preparations on set are missing and how they can be solved by studying different methods for match moving. The report focuses on these methods compared to the filmed material and how a vfx artist can choose a method to work with depending on that material. The methods that are discussed in this report are automatic tracking, tracking with feature points only and planar tracking. The results also show that different methods should be treated differently from each other by the vfx artist and that the result can differ depending on how the filmed material looks. Clarifying these methods and filmed material with tests will make it easier to choose a method to work with while match moving.

För att skapa kraftfullare presentationer måste nya metoder implementeras för att visualisera projekt. Ett aktivitetshus för ungdomar visualiseras i detta projekt genom att 3D-modellen av huset placeras i ett filmklipp från den plats där huset skall stå. Stegen som togs för att realisera detta var förarbete i form av inskaffande av ritningar, filmande, matchmoving, 3D-modellering, rendering, compositing och ljuddesign. Resultatet blev ett filmklipp som ur ett första persons perspektiv både visar exteriören och interiören.

<p>För att skapa kraftfullare presentationer måste nya metoder implementeras för att visualisera projekt. Ett aktivitetshus för ungdomar visualiseras i detta projekt genom att 3D-modellen av huset placeras i ett filmklipp från den plats där huset skall stå. Stegen som togs för att realisera detta var förarbete i form av inskaffande av ritningar, filmande, matchmoving, 3D-modellering, rendering, compositing och ljuddesign. Resultatet blev ett filmklipp som ur ett första persons perspektiv både visar exteriören och interiören.</p>

Den kvinnliga fotbollen har aldrig varit lika populär som den är idag och den fortsättergöra framsteg och lockar mer publik, samtidigt som flera tjejer/kvinnor börjar utövasporten. Tidigare forskning är begränsad gällande kvinnliga elitfotbollsspelare, merforskning inom kvinnlig fotboll och dess externa belastning kan hjälpa den kvinnligafotbollens utveckling. Kraven som ställs skiljer sig efter den position spelaren har, denexterna belastningen är en viktig del inom fotbollen. Fotbollen ställer krav på löpningari olika riktningar och i olika hastigheter, inbromsningar samt acceleration. Varjeposition tillämpar olika egenskaper. Syftet var att sammanställa kunskapen gällande denexterna belastningen hos kvinnliga elitfotbollsspelare. En systematisk deskriptivlitteraturöversikt med en systematisk sökning har använts för att besvara studiens syfte.Sökningen gjordes i databaserna Discovery och Pubmed och resulterade i 10vetenskapliga artiklar. En kvalitetsanalys utfördes på artiklarna, med hjälp av SBUmallar bedömdes 10 artiklar med poäng, av dessa 10 fick 4 artiklar 4 poäng, 1 fick 5poäng, 4 fick 6 poäng och 1 fick 7 poäng. Resultatet delades in i 3 teman: total distans iolika hastigheter, acceleration och deceleration samt topphastighet. De förstnämnda ochsistnämnda innebar att det skiljde sig beroende på position medan acceleration ochdeceleration syftade på hur många gånger det sker under en match men även om vilkazoner det sker i. Det ställs olika krav på spelarna beroende på position. De sekvensersom sker under en fotbollsmatch påverkas utifrån den position som spelaren har.Resultatet visade att kvinnliga elitfotbollsspelare har en totaldistans mellan 10–12 kmper match, utför mellan 400–1000 accelerationer och decelerationer och har engenomsnittlig topphastighet på ca 26 km/h. Sammanfattningsvis visar studien att denexterna belastningen som ställs på kvinnliga elitfotbollsspelare handlar om vilkenposition spelaren har. Resultatet ger även möjligheten för tränare att förbereda spelarnabättre inför match genom att anpassa träningsupplägget

Background: Video surveillance is a growing area where it can help with deterring crime, support investigation or to help gather statistics. These are just some areas where video surveillance can aid society. However, there is an improvement that could increase the efficiency of video surveillance by introducing tracking. More specifically, tracking between cameras in a network. Automating this process could reduce the need for humans to monitor and review since the tracking can track and inform the relevant people on its own. This has a wide array of usability areas, such as forensic investigation, crime alerting, or tracking down people who have disappeared. Objectives: What we want to investigate is the common setup of real-time multi-target multi-camera tracking (MTMCT) systems. Next up, we want to investigate how the components in an MTMCT system affect each other and the complete system. Lastly, we want to see how image enhancement can affect the MTMCT. Methods: To achieve our objectives, we have conducted a systematic literature review to gather information. Using the information, we implemented an MTMCT system where we evaluated the components to see how they interact in the complete system. Lastly, we implemented two image enhancement techniques to see how they affect the MTMCT. Results: As we have discovered, most often, MTMCT is constructed using a detection for discovering object, tracking to keep track of the objects in a single camera and a re-identification method to ensure that objects across cameras have the same ID. The different components have quite a considerable effect on each other where they can sabotage and improve each other. An example could be that the quality of the bounding boxes affect the data which re-identification can extract. We discovered that the image enhancement we used did not introduce any significant improvement. Conclusions: The most common structure for MTMCT are detection, tracking and re-identification. From our finding, we can see that all the component affect each other, but re-identification is the one that is mostly affected by the other components and the image enhancement. The two tested image enhancement techniques could not introduce enough improvement, but other image enhancement could be used to make the MTMCT perform better. The MTMCT system we constructed did not manage to reach real-time.

<p>Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace re-<br> search. Their rise in popularity spurred the growth of interest in urban air mobility (UAM)<br> across the world. UAM promises the next generation of transportation and logistics to be<br> handled by UASs that operate closer to where people live and work. Therefore safety and<br> security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially<br> challenging in urban canyon environments where traditional radar systems used for air traffic<br> control (ATC) are limited by their line of sight (LOS).<br> This thesis explores the design and preliminary results of a target tracking system for<br> urban canyon environments based on a network of camera nodes. A network of stationary<br> camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems<br> as well as cover considerable urban airspace. A camera node consists of a camera sensor, a<br> beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and<br> an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons,<br> an automatic calibration process of the proposed system is devised to simplify the time-<br> consuming and tedious calibration of a traditional camera network present in motion capture<br> (MoCap) systems. Through edge computing devices, the tracking system combines machine<br> learning techniques and motion detection as hybrid measurement modes for potential targets.<br> Then particle filters are used to estimate target tracks in real-time within the airspace from<br> measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking<br> volume shows an estimation error within 0.5m when tracking multiple targets. Moreover,<br> a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking<br> error within 0.3m on a micro-UAS in real time.</p>

In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tracking and event recognition for two-camera configurations are examined. Various foreground detection methods are discussed and a background modeling algorithm, which is based on multi-variate mixture of Gaussians, is proposed. During occlusion handling studies, a novel method for segmenting the occluded objects is proposed, in which a top-view of the scene, free of occlusions, is generated from multi-view data. The experiments indicate that the occlusion handling algorithm operates successfully on various test data. A novel tracking method by using multi-camera configurations is also proposed. The main idea of multi-camera employment is fusing the 2D information coming from the cameras to obtain a 3D information for better occlusion handling and seamless tracking. The proposed algorithm is tested on different data sets and it shows clear improvement over single camera tracker. Finally, multi-camera trajectories of objects are classified by proposed multi-camera event recognition method. In this method, concatenated different view trajectories are used to train Gaussian Mixture Hidden Markov Models. The experimental results indicate an improvement for the multi-camera event recognition performance over the event recognition by using single camera.

While the area of face recognition has been extensively studied in recent years, it remains a largely open problem, despite what movie and television studios would leave you to believe. Frontal, still face recognition research has seen a lot of success in recent years from any different researchers. However,the accuracy of such systems can be greatly diminished in cases such as increasing the variability of the database,occluding the face, and varying the illumination of the face. Further varying the pose of the face (yaw, pitch, and roll) and the face expression (smile, frown, etc.) adds even more complexity to the face recognition task, such as in the case of face recognition from video. In a more realistic video surveillance setting, a face recognition system should be robust to scale, pose, resolution, and occlusion as well as successfully track the face between frames. Also, a more advanced face recognition system should be able to improve the face recognition result by utilizing the information present in multiple video cameras. We approach the problem of face recognition from video in the following manner. We assume that the training data for the system consists of only still image data, such as passport photos or mugshots in a real-world system. We then transform the problem of face recognition from video to a still face recognition problem. Our research focuses on solutions to detecting, tracking and extracting face information from video frames so that they may be utilized effectively in a still face recognition system. We have developed four novel methods that assist in face recognition from video and multiple cameras. The first uses a patch-based method to handle the face recognition task when only patches, or parts, of the face are seen in a video, such as when occlusion of the face happens often. The second uses multiple cameras to fuse the recognition results of multiple cameras to improve the recognition accuracy. In the third solution, we utilize multiple overlapping video cameras to improve the face tracking result which thus improves the face recognition accuracy of the system. We additionally implement a methodology to detect and handle occlusion so that unwanted information is not used in the tracking algorithm. Finally, we introduce the average-half-face, which is shown to improve the results of still face recognition by utilizing the symmetry of the face. In one attempt to understand the use of the average-half-face in face recognition, an analysis of the effect of face symmetry on face recognition results is shown. / text

La création d'informations 3D denses à partir d'images endoscopiques intraopératoires, ainsi que le calcul de la position relative de la caméra endoscopique, sont des éléments fondamentaux pour un guidage de qualité durant la chirurgie guidée par l'image. Par exemple, cela permet de superposer modèle pré-opératoire via la réalité augmentée. Cette thèse présente une approche pour l'estimation de ces deux information basées sur une approche de localisation et cartographie simultanées (SLAM). Nous découplons la reconstruction dense de l'estimation de la trajectoire de la caméra, aboutissant à un système qui combine la précision du SLAM, et une reconstruction plus complète. Les solutions proposées dans cette thèse ont été validées sur de séquences porcines provenant de différents ensembles de données. Ces solutions n'ont pas besoin de matériel de suivi externe ni d'intervention. Les seules entrées nécessaires sont les trames vidéo d'un endoscope monoculaire. / Recovering dense 3D information from intra-operative endoscopic images together with the relative endoscope camera pose are fundamental blocks for accurate guidance and navigation in image-guided surgery. They have several important applications, e.g., augmented reality overlay of pre-operative models. This thesis provides a systematic approach for estimating these two pieces of information based on a pure vision Simultaneous Localization And Mapping (SLAM). We decouple the dense reconstruction from the camera trajectory estimation, resulting in a system that combines the accuracy and robustness of feature-based SLAM with the more complete reconstruction of direct SLAM methods. The proposed solutions in this thesis have been validated on real porcine sequences from different datasets and proved to be fast and do not need any external tracking hardware nor significant intervention from medical staff. The sole input is video frames of a standard monocular endoscope.

Cette thèse traite de la reconstruction tridimensionnelle d’une scène rigide à partir d’une collection de photographies numériques, dites vues. Le problème traité est connu sous le nom du "calcul de la structure et du mouvement" (structure-and/from-motion) qui consiste à "expliquer" des trajectoires de points dits d’intérêt au sein de la collection de vues par un certain mouvement de l’appareil (dont sa trajectoire) et des caractéristiques géométriques tridimensionnelles de la scène. Dans ce travail, nous proposons les fondements théoriques pour étendre certaines méthodes de calcul de la structure et du mouvement afin d’intégrer comme données d’entrée, des points d’intérêt réels et des points d’intérêt complexes, et plus précisément des images de points cycliques. Pour tout plan projectif, les points cycliques forment une paire de points complexes conjugués qui, par leur invariance par les similitudes planes, munissent le plan projectif d’une structure euclidienne. Nous introduisons la notion de marqueurs cycliques qui sont des marqueurs plans permettant de calculer sans ambiguïté les images des points cycliques de leur plan de support dans toute vue. Une propriété de ces marqueurs, en plus d’être très "riches" en information euclidienne, est que leurs images peuvent être appariées même si les marqueurs sont disposés arbitrairement sur des plans parallèles, grâce à l’invariance des points cycliques. Nous montrons comment utiliser cette propriété dans le calcul projectif de la structure et du mouvement via une technique matricielle de réduction de rang, dite de factorisation, de la matrice des données correspondant aux images de points réels, complexes et/ou cycliques. Un sous-problème critique abordé dans le calcul de la structure et du mouvement est celui de l’auto-calibrage de l’appareil, problème consistant à transformer un calcul projectif en un calcul euclidien. Nous expliquons comment utiliser l’information euclidienne fournie par les images des points cycliques dans l’algorithme d’auto-calibrage opérant dans l’espace projectif dual et fondé sur des équations linéaires. L’ensemble de ces contributions est finalement utilisé pour une application de suivi automatique de caméra utilisant des marqueurs formés par des couronnes concentriques (appelés CCTags), où il s’agit de calculer le mouvement tridimensionnel de la caméra dans la scène à partir d’une séquence vidéo. Ce type d’application est généralement utilisé dans l’industrie du cinéma ou de la télévision afin de produire des effets spéciaux. Le suivi de caméra proposé dans ce travail a été conçu pour proposer le meilleur compromis possible entre flexibilité d’utilisation et précision des résultats obtenus. / The thesis deals with the problem of 3D reconstruction of a rigid scene from a collection of views acquired by a digital camera. The problem addressed, referred as the Structure-from-Motion (SfM) problem, consists in computing the camera motion (including its trajectory) and the 3D characteristics of the scene based on 2D trajectories of imaged features through the collection. We propose theoretical foundations to extend some SfM paradigms in order to integrate real as well as complex imaged features as input data, and more especially imaged circular points. Circular points of a projective plane consist in a complex conjugate point-pair which is fixed under plane similarity ; thus endowing the plane with an Euclidean structure. We introduce the notion of circular markers which are planar markers that allows to compute, without any ambiguity, imaged circular points of their supporting plane in all views. Aside from providing a very “rich” Euclidean information, such features can be matched even if they are arbitrarily positioned on parallel planes thanks to their invariance under plane similarity ; thus increasing their visibility compared to natural features. We show how to benefit from this geometric property in solving the projective SfM problem via a rank-reduction technique, referred to as projective factorization, of the matrix whose entries are images of real, complex and/or circular features. One of the critical issues in such a SfM paradigm is the self-calibration problem, which consists in updating a projective reconstruction into an euclidean one. We explain how to use the euclidean information provided by imaged circular points in the self-calibration algorithm operating in the dual projective space and relying on linear equations. All these contributions are finally used in an automatic camera tracking application relying on markers made up of concentric circles (called C2Tags). The problem consists in computing the 3D camera motion based on a video sequence. This kind of application is generally used in the cinema or TV industry to create special effects. The camera tracking proposed in this work in designed in order to provide the best compromise between flexibility of use and accuracy.