Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Markerless structure-based multi-sensor calibration for free viewpoint video capture

Free-viewpoint capture technologies have recently started demonstrating impressive results. Being able to capture human performances in full 3D is a very promising technology for a variety of applications. However, the setup of the capturing infrastructure is usually expensive and requires trained personnel. In this work we focus on one practical aspect of setting up a free-viewpoint capturing system, the spatial alignment of the sensors. Our work aims at simplifying the external calibration process that typically requires significant human intervention and technical knowledge. Our method uses an easy to assemble structure and unlike similar works, does not rely on markers or features. Instead, we exploit the a-priori knowledge of the structure’s geometry to establish correspondences for the little-overlapping viewpoints typically found in free-viewpoint capture setups. These establish an initial sparse alignment that is then densely optimized. At the same time, our pipeline improves the robustness to assembly errors, allowing for non-technical users to calibrate multi-sensor setups. Our results showcase the feasibility of our approach that can make the tedious calibration process easier, and less error-prone

MONOCULAR POSE ESTIMATION AND SHAPE RECONSTRUCTION OF QUASI-ARTICULATED OBJECTS WITH CONSUMER DEPTH CAMERA

Quasi-articulated objects, such as human beings, are among the most commonly seen objects in our daily lives. Extensive research have been dedicated to 3D shape reconstruction and motion analysis for this type of objects for decades. A major motivation is their wide applications, such as in entertainment, surveillance and health care. Most of existing studies relied on one or more regular video cameras. In recent years, commodity depth sensors have become more and more widely available. The geometric measurements delivered by the depth sensors provide significantly valuable information for these tasks. In this dissertation, we propose three algorithms for monocular pose estimation and shape reconstruction of quasi-articulated objects using a single commodity depth sensor. These three algorithms achieve shape reconstruction with increasing levels of granularity and personalization. We then further develop a method for highly detailed shape reconstruction based on our pose estimation techniques. Our first algorithm takes advantage of a motion database acquired with an active marker-based motion capture system. This method combines pose detection through nearest neighbor search with pose refinement via non-rigid point cloud registration. It is capable of accommodating different body sizes and achieves more than twice higher accuracy compared to a previous state of the art on a publicly available dataset. The above algorithm performs frame by frame estimation and therefore is less prone to tracking failure. Nonetheless, it does not guarantee temporal consistent of the both the skeletal structure and the shape and could be problematic for some applications. To address this problem, we develop a real-time model-based approach for quasi-articulated pose and 3D shape estimation based on Iterative Closest Point (ICP) principal with several novel constraints that are critical for monocular scenario. In this algorithm, we further propose a novel method for automatic body size estimation that enables its capability to accommodate different subjects. Due to the local search nature, the ICP-based method could be trapped to local minima in the case of some complex and fast motions. To address this issue, we explore the potential of using statistical model for soft point correspondences association. Towards this end, we propose a unified framework based on Gaussian Mixture Model for joint pose and shape estimation of quasi-articulated objects. This method achieves state-of-the-art performance on various publicly available datasets. Based on our pose estimation techniques, we then develop a novel framework that achieves highly detailed shape reconstruction by only requiring the user to move naturally in front of a single depth sensor. Our experiments demonstrate reconstructed shapes with rich geometric details for various subjects with different apparels. Last but not the least, we explore the applicability of our method on two real-world applications. First of all, we combine our ICP-base method with cloth simulation techniques for Virtual Try-on. Our system delivers the first promising 3D-based virtual clothing system. Secondly, we explore the possibility to extend our pose estimation algorithms to assist physical therapist to identify their patients’ movement dysfunctions that are related to injuries. Our preliminary experiments have demonstrated promising results by comparison with the gold standard active marker-based commercial system. Throughout the dissertation, we develop various state-of-the-art algorithms for pose estimation and shape reconstruction of quasi-articulated objects by leveraging the geometric information from depth sensors. We also demonstrate their great potentials for different real-world applications

Non-contact measures to monitor hand movement of people with rheumatoid arthritis using a monocular RGB camera

Hand movements play an essential role in a person’s ability to interact with the environment. In hand biomechanics, the range of joint motion is a crucial metric to quantify changes due to degenerative pathologies, such as rheumatoid arthritis (RA). RA is a chronic condition where the immune system mistakenly attacks the joints, particularly those in the hands. Optoelectronic motion capture systems are gold-standard tools to quantify changes but are challenging to adopt outside laboratory settings. Deep learning executed on standard video data can capture RA participants in their natural environments, potentially supporting objectivity in remote consultation. The three main research aims in this thesis were 1) to assess the extent to which current deep learning architectures, which have been validated for quantifying motion of other body segments, can be applied to hand kinematics using monocular RGB cameras, 2) to localise where in videos the hand motions of interest are to be found, 3) to assess the validity of 1) and 2) to determine disease status in RA. First, hand kinematics for twelve healthy participants, captured with OpenPose were benchmarked against those captured using an optoelectronic system, showing acceptable instrument errors below 10°. Then, a gesture classifier was tested to segment video recordings of twenty-two healthy participants, achieving an accuracy of 93.5%. Finally, OpenPose and the classifier were applied to videos of RA participants performing hand exercises to determine disease status. The inferred disease activity exhibited agreement with the in-person ground truth in nine out of ten instances, outperforming virtual consultations, which agreed only six times out of ten. These results demonstrate that this approach is more effective than estimated disease activity performed by human experts during video consultations. The end goal sets the foundation for a tool that RA participants can use to observe their disease activity from their home.Open Acces

Assessing the Utility of a Video-Based Motion Capture Alternative in the Assessment of Lumbar Spine Planar Angular Joint Kinematics

Markerless motion capture is a novel technique to measure human movement kinematics. The purpose of this research is to evaluate the markerless algorithm, DeepLabCut (DLC) against a 3D motion capture system (Vicon Motion Systems Ltd., Oxford, UK) in the analysis of planar spine and elbow flexion-extension movement. Data were acquired concurrently from DLC and Vicon for all movements. A novel DLC model was trained using data derived from a subset of participants (training group). Accuracy and precision were assessed from data derived from the training group as well as in a new set of participants (testing group). Two-way SPM ANOVAs were used to detect significant differences between the training vs. testing sets, capture methods (Vicon vs. DLC), as well as potential higher order interaction effect between these independent variables in the estimation of flexion extension angles and variability. No significant differences were observed in any planar angles, nor were any higher order interactions observed between each motion capture modality and the training vs. testing datasets. Bland Altman plots were also generated to depict the mean bias and level of agreement between DLC and Vicon for both training, and testing datasets. Supplemental analyses, suggest that these results are partially affected by the alignment of each participant’s body segments with respect to each planar reference frame. This research suggests that DLC-derived planar kinematics of both the elbow and lumbar spine are of acceptable accuracy and precision when compared to conventional laboratory gold-standards (Vicon)

Interactive natural user interfaces

For many years, science fiction entertainment has showcased holographic technology and futuristic user interfaces that have stimulated the world\u27s imagination. Movies such as Star Wars and Minority Report portray characters interacting with free-floating 3D displays and manipulating virtual objects as though they were tangible. While these futuristic concepts are intriguing, it\u27s difficult to locate a commercial, interactive holographic video solution in an everyday electronics store. As used in this work, it should be noted that the term holography refers to artificially created, free-floating objects whereas the traditional term refers to the recording and reconstruction of 3D image data from 2D mediums. This research addresses the need for a feasible technological solution that allows users to work with projected, interactive and touch-sensitive 3D virtual environments. This research will aim to construct an interactive holographic user interface system by consolidating existing commodity hardware and interaction algorithms. In addition, this work studies the best design practices for human-centric factors related to 3D user interfaces. The problem of 3D user interfaces has been well-researched. When portrayed in science fiction, futuristic user interfaces usually consist of a holographic display, interaction controls and feedback mechanisms. In reality, holographic displays are usually represented by volumetric or multi-parallax technology. In this work, a novel holographic display is presented which leverages a mini-projector to produce a free-floating image onto a fog-like surface. The holographic user interface system will consist of a display component: to project a free-floating image; a tracking component: to allow the user to interact with the 3D display via gestures; and a software component: which drives the complete hardware system. After examining this research, readers will be well-informed on how to build an intuitive, eye-catching holographic user interface system for various application arenas