Tracking 3D human pose with large root node uncertainty

Representing articulated objects as a graphical model has gained much popularity in recent years, often the root node of the graph describes the global position and ori-entation of the object. In this work a method is presented to robustly track 3D human pose by permitting greater un-certainty to be modeled over the root node than existing techniques allow. Significantly, this is achieved without in-creasing the uncertainty of remaining parts of the model. The benefit is that a greater volume of the posterior can be supported making the approach less vulnerable to tracking failure. Given a hypothesis of the root node state a novel method is presented to estimate the posterior over the re-maining parts of the body conditioned on this value. All probability distributions are approximated using a single Gaussian allowing inference to be carried out in closed form. A set of deterministically selected sample points are used that allow the posterior to be updated for each part requiring just seven image likelihood evaluations making it extremely efficient. Multiple root node states are supported and propagated using standard sampling techniques. We believe this to be the first work devoted to efficient track-ing of human pose whilst modeling large uncertainty in the root node and demonstrate the presented method to be more robust to tracking failures than existing approaches. 1

Coping with uncertain dynamics in visual tracking : redundant state models and discrete search methods

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 133-142).A model of the world dynamics is a vital part of any tracking algorithm. The observed world can exhibit multiple complex dynamics at different spatio-temporal scales. Faithfully modeling all motion constraints in a computationally efficient manner may be too complicated or completely impossible. Resorting to use of approximate motion models complicates tracking by making it less robust to unmodeled noise and increasing running times. We propose two complimentary approaches to tracking with approximate dynamic models in a probabilistic setting. The Redundant State Multi-Chain Model formalism described in the first part of the thesis allows combining multiple weak motion models, each representing a particular aspect of overall dynamic, in a cooperative manner to improve state estimates. This is applicable, in particular, to hierarchical machine vision systems that combine trackers at several spatio-temporal scales. In the second part of the dissertation, we propose supplementing exploration of the continuous likelihood surface with the discrete search in a fixed set of points distributed through the state space. We demonstrate the utility of these approaches on a range of machine vision problems: adaptive background subtraction, structure from motion estimation, and articulated body tracking.by Leonid Taycher.Ph.D

Efficient Human Pose Estimation with Image-dependent Interactions

Human pose estimation from 2D images is one of the most challenging and computationally-demanding problems in computer vision. Standard models such as Pictorial Structures consider interactions between kinematically connected joints or limbs, leading to inference cost that is quadratic in the number of pixels. As a result, researchers and practitioners have restricted themselves to simple models which only measure the quality of limb-pair possibilities by their 2D geometric plausibility. In this talk, we propose novel methods which allow for efficient inference in richer models with data-dependent interactions. First, we introduce structured prediction cascades, a structured analog of binary cascaded classifiers, which learn to focus computational effort where it is needed, filtering out many states cheaply while ensuring the correct output is unfiltered. Second, we propose a way to decompose models of human pose with cyclic dependencies into a collection of tree models, and provide novel methods to impose model agreement. Finally, we develop a local linear approach that learns bases centered around modes in the training data, giving us image-dependent local models which are fast and accurate. These techniques allow for sparse and efficient inference on the order of minutes or seconds per image. As a result, we can afford to model pairwise interaction potentials much more richly with data-dependent features such as contour continuity, segmentation alignment, color consistency, optical flow and multiple modes. We show empirically that these richer models are worthwhile, obtaining significantly more accurate pose estimation on popular datasets

Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces

Inferring Human Pose and Motion from Images

As optical gesture recognition technology advances, touchless human computer interfaces of the future will soon become a reality. One particular technology, markerless motion capture, has gained a large amount of attention, with widespread application in diverse disciplines, including medical science, sports analysis, advanced user interfaces, and virtual arts. However, the complexity of human anatomy makes markerless motion capture a non-trivial problem: I) parameterised pose configuration exhibits high dimensionality, and II) there is considerable ambiguity in surjective inverse mapping from observation to pose configuration spaces with a limited number of camera views. These factors together lead to multimodality in high dimensional space, making markerless motion capture an ill-posed problem. This study challenges these difficulties by introducing a new framework. It begins with automatically modelling specific subject template models and calibrating posture at the initial stage. Subsequent tracking is accomplished by embedding naturally-inspired global optimisation into the sequential Bayesian filtering framework. Tracking is enhanced by several robust evaluation improvements. Sparsity of images is managed by compressive evaluation, further accelerating computational efficiency in high dimensional space

Robot Navigation in Distorted Magnetic Fields

This thesis investigates the utilization of magnetic field distortions for the localization and navigation of robotic systems. The work comprehensively illuminates the various aspects that are relevant in this context. Among other things, the characteristics of magnetic field environments are assessed and examined for their usability for robot navigation in various typical mobile robot deployment scenarios. A strong focus of this work lies in the self-induced static and dynamic magnetic field distortions of complex kinematic robots, which could hinder the use of magnetic fields because of their interference with the ambient magnetic field. In addition to the examination of typical distortions in robots of different classes, solutions for compensation and concrete tools are developed both in hardware (distributed magnetometer sensor systems) and in software. In this context, machine learning approaches for learning static and dynamic system distortions are explored and contrasted with classical methods for calibrating magnetic field sensors. In order to extend probabilistic state estimation methods towards the localization in magnetic fields, a measurement model based on Mises-Fisher distributions is developed in this thesis. Finally, the approaches of this work are evaluated in practice inside and outside the laboratory in different environments and domains (e.g. office, subsea, desert, etc.) with different types of robot systems

Uncertainty and social considerations for mobile assistive robot navigation

An increased interest in mobile robots has been seen over the past years. The wide range of possible applications, from vacuum cleaners to assistant robots, makes such robots an interesting solution to many everyday problems. A key requirement for the mass deployment of such robots is to ensure they can safely navigate around our daily living environments. A robot colliding with or bumping into a person may be, in some contexts, unacceptable. For example, if a robot working around elderly people collides with one of them, it may cause serious injuries. This thesis explores four major components required for effective robot navigation: sensing the static environment, detection and tracking of moving people, obstacle and people avoidance with uncertainty measurement, and basic social navigation considerations. First, to guarantee adherence to basic safety constraints, sensors and algorithms required to measure the complex structure of our daily living environments are explored. Not only do the static components of the environment have to be measured, but so do any people present. A people detection and tracking algorithm, aimed for a crowded environment is proposed, thus enhancing the robot's perception capabilities. Our daily living environments present many inherent sources of uncertainty for robots, one of them arising due to the robot's inability to know people's intentions as they move. To solve this problem, a motion model that assumes unknown long-term intentions is proposed. This is used in conjunction with a novel uncertainty aware local planner to create feasible trajectories. In social situations, the presence of groups of people cannot be neglected when navigating. To avoid the robot interrupting groups of people, it first needs to be able to detect such groups. A group detector is proposed which relies on a set of gaze- and geometric-based features. Avoiding group disruption is finally incorporated into the navigation algorithm by means of taking into account the probability of disrupting a group's activities. The effectiveness of the four different components is evaluated using real world and simulated data, demonstrating the benefits for mobile robot navigation.Open Acces