Understanding egocentric human actions with temporal decision forests

Understanding human actions is a fundamental task in computer vision with a wide range of applications including pervasive health-care, robotics and game control. This thesis focuses on the problem of egocentric action recognition from RGB-D data, wherein the world is viewed through the eyes of the actor whose hands describe the actions. The main contributions of this work are its findings regarding egocentric actions as described by hands in two application scenarios and a proposal of a new technique that is based on temporal decision forests. The thesis first introduces a novel framework to recognise fingertip writing in mid-air in the context of human-computer interaction. This framework detects whether the user is writing and tracks the fingertip over time to generate spatio-temporal trajectories that are recognised by using a Hough forest variant that encourages temporal consistency in prediction. A problem with using such forest approach for action recognition is that the learning of temporal dynamics is limited to hand-crafted temporal features and temporal regression, which may break the temporal continuity and lead to inconsistent predictions. To overcome this limitation, the thesis proposes transition forests. Besides any temporal information that is encoded in the feature space, the forest automatically learns the temporal dynamics during training, and it is exploited in inference in an online and efficient manner achieving state-of-the-art results. The last contribution of this thesis is its introduction of the first RGB-D benchmark to allow for the study of egocentric hand-object actions with both hand and object pose annotations. This study conducts an extensive evaluation of different baselines, state-of-the art approaches and temporal decision forest models using colour, depth and hand pose features. Furthermore, it extends the transition forest model to incorporate data from different modalities and demonstrates the benefit of using hand pose features to recognise egocentric human actions. The thesis concludes by discussing and analysing the contributions and proposing a few ideas for future work.Open Acces

Modelling human pose and shape based on a database of human 3D scans

Generating realistic human shapes and motion is an important task both in the motion picture industry and in computer games. In feature films, high quality and believability are the most important characteristics. Additionally, when creating virtual doubles the generated charactes have to match as closely as possible to given real persons. In contrast, in computer games the level of realism does not need to be as high but real-time performance is essential. It is desirable to meet all these requirements with a general model of human pose and shape. In addition, many markerless human tracking methods applied, e.g., in biomedicine or sports science can benefit greatly from the availability of such a model because most methods require a 3D model of the tracked subject as input, which can be generated on-the-fly given a suitable shape and pose model. In this thesis, a comprehensive procedure is presented to generate different general models of human pose. A database of 3D scans spanning the space of human pose and shape variations is introduced. Then, four different approaches for transforming the database into a general model of human pose and shape are presented, which improve the current state of the art. Experiments are performed to evaluate and compare the proposed models on real-world problems, i.e., characters are generated given semantic constraints and the underlying shape and pose of humans given 3D scans, multi-view video, or uncalibrated monocular images is estimated.Die Erzeugung realistischer Menschenmodelle ist eine wichtige Anwendung in der Filmindustrie und bei Computerspielen. In Spielen ist Echtzeitsynthese unabdingbar aber der Detailgrad muß nicht so hoch sein wie in Filmen. Für virtuelle Doubles, wie sie z.B. in Filmen eingesetzt werden, muss der generierte Charakter dem gegebenen realen Menschen möglichst ähnlich sein. Mit einem generellen Modell für menschliche Pose und Körperform ist es möglich alle diese Anforderungen zu erfüllen. Zusätzlich können viele Verfahren zur markerlosen Bewegungserfassung, wie sie z.B. in der Biomedizin oder in den Sportwissenschaften eingesetzt werden, von einem generellen Modell für Pose und Körperform profitieren. Da diese ein 3D Modell der erfassten Person benötigen, das jetzt zur Laufzeit generiert werden kann. In dieser Doktorarbeit wird ein umfassender Ansatz vorgestellt, um verschiedene Modelle für Pose und Körperform zu berechnen. Zunächst wird eine Datenbank von 3D Scans aufgebaut, die Pose- und Körperformvariationen von Menschen umfasst. Dann werden vier verschiedene Verfahren eingeführt, die daraus generelle Modelle für Pose und Körperform berechnen und Probleme beim Stand der Technik beheben. Die vorgestellten Modelle werden auf realistischen Problemstellungen getestet. So werden Menschenmodelle aus einigen wenigen Randbedingungen erzeugt und Pose und Körperform von Probanden wird aus 3D Scans, Multi-Kamera Videodaten und Einzelbildern der bekleideten Personen geschätzt

Model-Based High-Dimensional Pose Estimation with Application to Hand Tracking

This thesis presents novel techniques for computer vision based full-DOF human hand motion estimation. Our main contributions are: A robust skin color estimation approach; A novel resolution-independent and memory efficient representation of hand pose silhouettes, which allows us to compute area-based similarity measures in near-constant time; A set of new segmentation-based similarity measures; A new class of similarity measures that work for nearly arbitrary input modalities; A novel edge-based similarity measure that avoids any problematic thresholding or discretizations and can be computed very efficiently in Fourier space; A template hierarchy to minimize the number of similarity computations needed for finding the most likely hand pose observed; And finally, a novel image space search method, which we naturally combine with our hierarchy. Consequently, matching can efficiently be formulated as a simultaneous template tree traversal and function maximization

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier

Hierarchische Modelle für das visuelle Erkennen und Lernen von Objekten, Szenen und Aktivitäten

In many computer vision applications, objects have to be learned and recognized in images or image sequences. Most of these objects have a hierarchical structure.For example, 3d objects can be decomposed into object parts, and object parts, in turn, into geometric primitives. Furthermore, scenes are composed of objects. And also activities or behaviors can be divided hierarchically into actions, these into individual movements, etc. Hierarchical models are therefore ideally suited for the representation of a wide range of objects used in applications such as object recognition, human pose estimation, or activity recognition. In this work new probabilistic hierarchical models are presented that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects, object parts or actions and movements in order to share calculations and avoid redundant information. We will introduce online and offline learning methods, which enable to create efficient hierarchies based on small or large training datasets, in which poses or articulated structures are given by instances. Furthermore, we present inference approaches for fast and robust detection. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. They will be used in an unified hierarchical framework spatially for object recognition as well as spatiotemporally for activity recognition. The unified generic hierarchical framework allows us to apply the proposed models in different projects. Besides classical object recognition it is used for detection of human poses in a project for gait analysis. The activity detection is used in a project for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.In zahlreichen Computer Vision Anwendungen müssen Objekte in einzelnen Bildern oder Bildsequenzen erlernt und erkannt werden. Viele dieser Objekte sind hierarchisch aufgebaut.So lassen sich 3d Objekte in Objektteile zerlegen und Objektteile wiederum in geometrische Grundkörper. Und auch Aktivitäten oder Verhaltensmuster lassen sich hierarchisch in einzelne Aktionen aufteilen, diese wiederum in einzelne Bewegungen usw. Für die Repräsentation sind hierarchische Modelle dementsprechend gut geeignet. In dieser Arbeit werden neue probabilistische hierarchische Modelle vorgestellt, die es ermöglichen auch mehrere Objekte verschiedener Kategorien, Skalierungen, Rotationen und aus verschiedenen Blickrichtungen effizient zu repräsentieren. Eine Idee ist hierbei, Ähnlichkeiten unter Objekten, Objektteilen oder auch Aktionen und Bewegungen zu nutzen, um redundante Informationen und Mehrfachberechnungen zu vermeiden. In der Arbeit werden online und offline Lernverfahren vorgestellt, die es ermöglichen, effiziente Hierarchien auf Basis von kleinen oder großen Trainingsdatensätzen zu erstellen, in denen Posen und bewegliche Strukturen durch Beispiele gegeben sind. Des Weiteren werden Inferenzansätze zur schnellen und robusten Detektion vorgestellt. Diese werden innerhalb eines einheitlichen hierarchischen Frameworks sowohl räumlich zur Objekterkennung als auch raumzeitlich zur Aktivitätenerkennung verwendet. Das einheitliche Framework ermöglicht die Anwendung des vorgestellten Modells innerhalb verschiedener Projekte. Neben der klassischen Objekterkennung wird es zur Erkennung von menschlichen Posen in einem Projekt zur Ganganalyse verwendet. Die Aktivitätenerkennung wird in einem Projekt zur Gestaltung altersgerechter Lebenswelten genutzt, um in intelligenten Wohnräumen Aktivitäten und Verhaltensmuster von Bewohnern zu erkennen. Im Rahmen eines Projektes zur Parklückenvermessung mithilfe eines intelligenten Fahrzeuges werden die vorgestellten Ansätze verwendet, um das Umfeld des Fahrzeuges hierarchisch zu modellieren und dadurch das Szenenverstehen zu ermöglichen

Human pose estimation from video and inertial sensors

[no abstract

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

Active recognition and pose estimation of rigid and deformable objects in 3D space

Object recognition and pose estimation is a fundamental problem in computer vision and of utmost importance in robotic applications. Object recognition refers to the problem of recognizing certain object instances, or categorizing objects into specific classes. Pose estimation deals with estimating the exact position of the object in 3D space, usually expressed in Euler angles. There are generally two types of objects that require special care when designing solutions to the aforementioned problems: rigid and deformable. Dealing with deformable objects has been a much harder problem, and usually solutions that apply to rigid objects, fail when used for deformable objects due to the inherent assumptions made during the design. In this thesis we deal with object categorization, instance recognition and pose estimation of both rigid and deformable objects. In particular, we are interested in a special type of deformable objects, clothes. We tackle the problem of autonomously recognizing and unfolding articles of clothing using a dual manipulator. This problem consists of grasping an article from a random point, recognizing it and then bringing it into an unfolded state by a dual arm robot. We propose a data-driven method for clothes recognition from depth images using Random Decision Forests. We also propose a method for unfolding an article of clothing after estimating and grasping two key-points, using Hough Forests. Both methods are implemented into a POMDP framework allowing the robot to interact optimally with the garments, taking into account uncertainty in the recognition and point estimation process. This active recognition and unfolding makes our system very robust to noisy observations. Our methods were tested on regular-sized clothes using a dual-arm manipulator. Our systems perform better in both accuracy and speed compared to state-of-the-art approaches. In order to take advantage of the robotic manipulator and increase the accuracy of our system, we developed a novel approach to address generic active vision problems, called Active Random Forests. While state of the art focuses on best viewing parameters selection based on single view classifiers, we propose a multi-view classifier where the decision mechanism of optimally changing viewing parameters is inherent to the classification process. This has many advantages: a) the classifier exploits the entire set of captured images and does not simply aggregate probabilistically per view hypotheses; b) actions are based on learnt disambiguating features from all views and are optimally selected using the powerful voting scheme of Random Forests and c) the classifier can take into account the costs of actions. The proposed framework was applied to the same task of autonomously unfolding clothes by a robot, addressing the problem of best viewpoint selection in classification, grasp point and pose estimation of garments. We show great performance improvement compared to state of the art methods and our previous POMDP formulation. Moving from deformable to rigid objects while keeping our interest to domestic robotic applications, we focus on object instance recognition and 3D pose estimation of household objects. We are particularly interested in realistic scenes that are very crowded and objects can be perceived under severe occlusions. Single shot-based 6D pose estimators with manually designed features are still unable to tackle such difficult scenarios for a variety of objects, motivating the research towards unsupervised feature learning and next-best-view estimation. We present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve 6D object pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets. Unsupervised feature learning, although efficient, might produce sub-optimal features for our particular tast. Therefore in our last work, we leverage the power of Convolutional Neural Networks to tackled the problem of estimating the pose of rigid objects by an end-to-end deep regression network. To improve the moderate performance of the standard regression objective function, we introduce the Siamese Regression Network. For a given image pair, we enforce a similarity measure between the representation of the sample images in the feature and pose space respectively, that is shown to boost regression performance. Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art. Last, our feature learning formulation provides the ability of learning features that can perform under severe occlusions, demonstrating high performance on our novel hand-object dataset. Concluding, this work is a research on the area of object detection and pose estimation in 3D space, on a variety of object types. Furthermore we investigate how accuracy can be further improved by applying active vision techniques to optimally move the camera view to minimize the detection error.Open Acces

Learning deep representations for robotics applications

In this thesis, two hierarchical learning representations are explored in computer vision tasks. First, a novel graph theoretic method for statistical shape analysis, called Compositional Hierarchy of Parts (CHOP), was proposed. The method utilises line-based features as its building blocks for the representation of shapes. A deep, multi-layer vocabulary is learned by recursively compressing this initial representation. The key contribution of this work is to formulate layerwise learning as a frequent sub-graph discovery problem, solved using the Minimum Description Length (MDL) principle. The experiments show that CHOP employs part shareability and data compression features, and yields state-of- the-art shape retrieval performance on 3 benchmark datasets. In the second part of the thesis, a hybrid generative-evaluative method was used to solve the dexterous grasping problem. This approach combines a learned dexterous grasp generation model with two novel evaluative models based on Convolutional Neural Networks (CNNs). The data- efficient generative method learns from a human demonstrator. The evaluative models are trained in simulation, using the grasps proposed by the generative approach and the depth images of the objects from a single view. On a real grasp dataset of 49 scenes with previously unseen objects, the proposed hybrid architecture outperforms the purely generative method, with a grasp success rate of 77.7% to 57.1%. The thesis concludes by comparing the two families of deep architectures, compositional hierarchies and DNNs, providing insights on their strengths and weaknesses