On the Two-View Geometry of Unsynchronized Cameras

We present new methods for simultaneously estimating camera geometry and time shift from video sequences from multiple unsynchronized cameras. Algorithms for simultaneous computation of a fundamental matrix or a homography with unknown time shift between images are developed. Our methods use minimal correspondence sets (eight for fundamental matrix and four and a half for homography) and therefore are suitable for robust estimation using RANSAC. Furthermore, we present an iterative algorithm that extends the applicability on sequences which are significantly unsynchronized, finding the correct time shift up to several seconds. We evaluated the methods on synthetic and wide range of real world datasets and the results show a broad applicability to the problem of camera synchronization.Comment: 12 pages, 9 figures, Computer Vision and Pattern Recognition (CVPR) 201

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

Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

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

3D human motion sequences synchronization using dense matching algorithm

Annual Symposium of the German Association for Pattern Recognition (DAGM), 2006, Berlin (Germany)This work solves the problem of synchronizing pre-recorded human motion sequences, which show different speeds and accelerations, by using a novel dense matching algorithm. The approach is based on the dynamic programming principle that allows finding an optimal solution very fast. Additionally, an optimal sequence is automatically selected from the input data set to be a time scale pattern for all other sequences. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe

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

Automatic learning of 3D pose variability in walking performances for gait analysis

This paper proposes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. First, a Dynamic Programing synchronization algorithm is presented in order to establish a mapping between postures from different walking cycles, so the whole training set can be synchronized to a common time pattern. Then, the model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally statistics about the observed variability of the postures and motion direction are also computed at each time step. As a result, in this work we have extended a similar action model successfully used for tracking, by providing facilities for gait analysis and gait recognition applications.Peer ReviewedPreprin

Slice Matching for Accurate Spatio-Temporal Alignment

International audienceVideo synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately co-incident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and re- fine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works

Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements

Our world is filled with living beings and other dynamic elements. It is important to record dynamic things and events for the sake of education, archeology, and culture inheritance. From vintage to modern times, people have recorded dynamic scene elements in different ways, from sequences of cave paintings to frames of motion pictures. This thesis focuses on two key computer vision techniques by which dynamic element representation moves beyond video capture: towards 3D reconstruction and view synthesis. Although previous methods on these two aspects have been adopted to model and represent static scene elements, dynamic scene elements present unique and difficult challenges for the tasks. This thesis focuses on three types of dynamic scene elements, namely 1) dynamic texture with static shape, 2) dynamic shapes with static texture, and 3) dynamic illumination of static scenes. Two research aspects will be explored to represent and visualize them: dynamic 3D reconstruction and dynamic view synthesis. Dynamic 3D reconstruction aims to recover the 3D geometry of dynamic objects and, by modeling the objects’ movements, bring 3D reconstructions to life. Dynamic view synthesis, on the other hand, summarizes or predicts the dynamic appearance change of dynamic objects – for example, the daytime-to-nighttime illumination of a building or the future movements of a rigid body. We first target the problem of reconstructing dynamic textures of objects that have (approximately) fixed 3D shape but time-varying appearance. Examples of such objects include waterfalls, fountains, and electronic billboards. Since the appearance of dynamic-textured objects can be random and complicated, estimating the 3D geometry of these objects from 2D images/video requires novel tools beyond the appearance-based point correspondence methods of traditional 3D computer vision. To perform this 3D reconstruction, we introduce a method that simultaneously 1) segments dynamically textured scene objects in the input images and 2) reconstructs the 3D geometry of the entire scene, assuming a static 3D shape for the dynamically textured objects. Compared to dynamic textures, the appearance change of dynamic shapes is due to physically defined motions like rigid body movements. In these cases, assumptions can be made about the object’s motion constraints in order to identify corresponding points on the object at different timepoints. For example, two points on a rigid object have constant distance between them in the 3D space, no matter how the object moves. Based on this assumption of local rigidity, we propose a robust method to correctly identify point correspondences of two images viewing the same moving object from different viewpoints and at different times. Dense 3D geometry could be obtained from the computed point correspondences. We apply this method on unsynchronized video streams, and observe that the number of inlier correspondences found by this method can be used as indicator for frame alignment among the different streams. To model dynamic scene appearance caused by illumination changes, we propose a framework to find a sequence of images that have similar geometric composition as a single reference image and also show a smooth transition in illumination throughout the day. These images could be registered to visualize patterns of illumination change from a single viewpoint. The final topic of this thesis involves predicting the movements of dynamic shapes in the image domain. Towards this end, we propose deep neural network architectures to predict future views of dynamic motions, such as rigid body movements and flowers blooming. Instead of predicting image pixels from the network, my methods predict pixel offsets and iteratively synthesize future views.Doctor of Philosoph