Formalization of the General Video Temporal Synchronization Problem

In this work, we present a theoretical formalization of the temporal synchronization problem and a method to temporally synchronize multiple stationary video cameras with overlapping views of the same scene. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying curvature points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find frames that best agree with the pre-computed camera geometries from stationary background image features. By using the fundamental matrix and the trifocal tensor in the second refinement step, we improve the estimation of the first step and handle a broader more generic range of input scenarios and camera conditions. The method is relatively simple compared to current techniques and is no harder than feature tracking in stage one and computing accurate geometries in stage two. We also provide a robust method to assist synchronization in the presence of inaccurate geometry computation, and a theoretical limit on the accuracy that can be expected from any synchronization syste

Modélisation 4D à partir de plusieurs caméras

Les systèmes multi-caméras permettent de nos jours d'obtenir à la fois des flux d'images couleur mais aussi des flux de modèles 3D. Ils permettent ainsi l'étude de scènes complexes à la fois de par les éléments qui la composent mais aussi de par les mouvements et les déformations que subissent ces éléments au fil du temps. Une des principales limitations de ces données est le manque de cohérence temporelle entre les observations obtenues à deux instants de temps successifs. Les travaux présentés dans cette thèse proposent des pistes pour retrouver cette cohérence temporelle. Dans un premier temps nous nous sommes penchés sur le problème de l'estimation de champs de déplacement denses à la surface des objets de la scène. L'approche que nous proposons permet de combiner efficacement des informations photométriques provenant des caméras avec des informations géométriques. Cette méthode a été étendue, par la suite, au cas de systèmes multi-caméras hybrides composés de capteurs couleurs et de profondeur (tel que le capteur kinect). Dans un second temps nous proposons une méthode nouvelle permettant l'apprentissage de la vraie topologie d'une scène dynamique au fil d'une séquence de données 4D (3D + temps). Ces travaux permettent de construire au fur et à mesure des observations un modèle de référence de plus en plus complet de la scène observée.Nowadays mutli-camera setups allow the acquisition of both color image streams and 3D models streams. Thus permitting the study of complex scenes. These scenes can be composed of any number of non-rigid objects moving freely. One of the main limitations of such data is its lack of temporal coherence between two consecutive observations. The work presented in this thesis consider this issue and propose novel methods to recover this temporal coherence. First we present a new approach that computes at each frame a dense motion field over the surface of the scene (i.e. Scene Flow), gathering both photometric and geometric information. We then extend this approach to hybrid multi-camera setups composed of color and depth sensor (such as the kinect sensor). Second, we introduce "Progressive Shape Models", a new method that allows to gather topology information over a complete sequence of 3D models and incrementally build a complete and coherent surface template.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Marker-less motion capture in general scenes with sparse multi-camera setups

Human motion-capture from videos is one of the fundamental problems in computer vision and computer graphics. Its applications can be found in a wide range of industries. Even with all the developments in the past years, industry and academia alike still rely on complex and expensive marker-based systems. Many state-of-the-art marker-less motioncapture methods come close to the performance of marker-based algorithms, but only when recording in highly controlled studio environments with exactly synchronized, static and sufficiently many cameras. While relative to marker-based systems, this yields an easier apparatus with a reduced setup time, the hurdles towards practical application are still large and the costs are considerable. By being constrained to a controlled studio, marker-less methods fail to fully play out their advantage of being able to capture scenes without actively modifying them. In the area of marker-less human motion-capture, this thesis proposes several novel algorithms for simplifying the motion-capture to be applicable in new general outdoor scenes. The first is an optical multi-video synchronization method which achieves subframe accuracy in general scenes. In this step, the synchronization parameters of multiple videos are estimated. Then, we propose a spatio-temporal motion-capture method which uses the synchronization parameters for accurate motion-capture with unsynchronized cameras. Afterwards, we propose a motion capture method that works with moving cameras, where multiple people are tracked even in front of cluttered and dynamic backgrounds with potentially moving cameras. Finally, we reduce the number of cameras employed by proposing a novel motion-capture method which uses as few as two cameras to capture high-quality motion in general environments, even outdoors. The methods proposed in this thesis can be adopted in many practical applications to achieve similar performance as complex motion-capture studios with a few consumer-grade cameras, such as mobile phones or GoPros, even for uncontrolled outdoor scenes.Die videobasierte Bewegungserfassung (Motion Capture) menschlicher Darsteller ist ein fundamentales Problem in Computer Vision und Computergrafik, das in einer Vielzahl von Branchen Anwendung findet. Trotz des Fortschritts der letzten Jahre verlassen sich Wirtschaft und Wissenschaft noch immer auf komplexe und teure markerbasierte Systeme. Viele aktuelle markerlose Motion-Capture-Verfahren kommen der Leistung von markerbasierten Algorithmen nahe, aber nur bei Aufnahmen in stark kontrollierten Studio-Umgebungen mit genügend genau synchronisierten, statischen Kameras. Im Vergleich zu markerbasierten Systemen wird der Aufbau erheblich vereinfacht, was Zeit beim Aufbau spart, aber die Hürden für die praktische Anwendung sind noch immer groß und die Kosten beträchtlich. Durch die Beschränkung auf ein kontrolliertes Studio können markerlose Verfahren nicht vollständig ihren Vorteil ausspielen, Szenen aufzunehmen zu können, ohne sie aktiv zu verändern. Diese Arbeit schlägt mehrere neuartige markerlose Motion-Capture-Verfahren vor, welche die Erfassung menschlicher Darsteller in allgemeinen Außenaufnahmen vereinfachen. Das erste ist ein optisches Videosynchronisierungsverfahren, welches die Synchronisationsparameter mehrerer Videos genauer als die Bildwiederholrate schätzt. Anschließend wird ein Raum-Zeit-Motion-Capture-Verfahren vorgeschlagen, welches die Synchronisationsparameter für präzises Motion Capture mit nicht synchronisierten Kameras verwendet. Außerdem wird ein Motion-Capture-Verfahren für bewegliche Kameras vorgestellt, das mehrere Menschen auch vor unübersichtlichen und dynamischen Hintergründen erfasst. Schließlich wird die Anzahl der erforderlichen Kameras durch ein neues MotionCapture-Verfahren, auf lediglich zwei Kameras reduziert, um Bewegungen qualitativ hochwertig auch in allgemeinen Umgebungen wie im Freien zu erfassen. Die in dieser Arbeit vorgeschlagenen Verfahren können in viele praktische Anwendungen übernommen werden, um eine ähnliche Leistung wie komplexe Motion-Capture-Studios mit lediglich einigen Videokameras der Verbraucherklasse, zum Beispiel Mobiltelefonen oder GoPros, auch in unkontrollierten Außenaufnahmen zu erzielen

Multi-view dynamic scene modeling

Modeling dynamic scenes/events from multiple fixed-location vision sensors, such as video camcorders, infrared cameras, Time-of-Flight sensors etc, is of broad interest in computer vision society, with many applications including 3D TV, virtual reality, medical surgery, markerless motion capture, video games, and security surveillance. However, most of the existing multi-view systems are set up in a strictly controlled indoor environment, with fixed lighting conditions and simple background views. Many challenges are preventing the technology to an outdoor natural environment. These include varying sunlight, shadows, reflections, background motion and visual occlusion. In this thesis, I address different aspects to overcome all of the aforementioned difficulties, so as to reduce human preparation and manipulation, and to make a robust outdoor system as automatic as possible. In particular, the main novel technical contributions of this thesis are as follows: a generic heterogeneous sensor fusion framework for robust 3D shape estimation together; a way to automatically recover 3D shapes of static occluder from dynamic object silhouette cues, which explicitly models the static visual occluding event along the viewing rays; a system to model multiple dynamic objects shapes and track their identities simultaneously, which explicitly models the inter-occluding event between dynamic objects; a scheme to recover an object's dense 3D motion flow over time, without assuming any prior knowledge of the underlying structure of the dynamic object being modeled, which helps to enforce temporal consistency of natural motions and initializes more advanced shape learning and motion analysis. A unified automatic calibration algorithm for the heterogeneous network of conventional cameras/camcorders and new Time-of-Flight sensors is also proposed