The Alignment Between 3-D Data and Articulated Shapes with Bending Surfaces

International audienceIn this paper we address the problem of aligning 3-D data with articulated shapes. This problem resides at the core of many motion tracking methods with applications in human motion capture, action recognition, medical-image analysis, etc. We describe an articulated and bending surface representation well suited for this task as well as a method which aligns (or registers) such a surface to 3-D data. Articulated objects, e.g., humans and animals, are covered with clothes and skin which may be seen as textured surfaces. These surfaces are both articulated and deformable and one realistic way to model them is to assume that they bend in the neighborhood of the shape's joints. We will introduce a surface-bending model as a function of the articulated-motion parameters. This combined articulated-motion and surface-bending model better predicts the observed phenomena in the data and therefore is well suited for surface registration. Given a set of sparse 3-D data (gathered with a stereo camera pair) and a textured, articulated, and bending surface, we describe a register-and-fit method that proceeds as follows. First, the data-to-surface registration problem is formalized as a classifier and is carried out using an EM algorithm. Second, the data-to-surface fitting problem is carried out by minimizing the distance from the registered data points to the surface over the joint variables. In order to illustrate the method we applied it to the problem of hand tracking. A hand model with 27 degrees of freedom is successfully registered and fitted to a sequence of 3-D data points gathered with a stereo camera pair

Human Motion Tracking by Registering an Articulated Surface to 3-D Points and Normals

We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of a kinematic human-body representation, as well as probabilities that the data are assigned either to a body part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes

Tracking Articulated Bodies using Generalized Expectation Maximization

A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analysis, the configuration that gives the best match to the input image is held as estimate for the current frame. This match is computed iterating GEM to assign edge pixels to the correct body part and to find the body pose that maximizes the likelihood of the assignments

Practical color-based motion capture

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 93-101).Motion capture systems track the 3-D pose of the human body and are widely used for high quality content creation, gestural user input and virtual reality. However, these systems are rarely deployed in consumer applications due to their price and complexity. In this thesis, we propose a motion capture system built from commodity components that can be deployed in a matter of minutes. Our approach uses one or more webcams and a color garment to track either the user's upper body or hands for motion capture and user input. We demonstrate that custom designed color garments can simplify difficult computer vision problems and lead to efficient and robust algorithms for hand and upper body tracking. Specifically, our highly descriptive color patterns alleviate ambiguities that are commonly encountered when tracking only silhouettes or edges, allowing us to employ a nearest-neighbor approach to track either the hands or the upper body at interactive rates. We also describe a robust color calibration system that enables our color-based tracking to work against cluttered backgrounds and under multiple illuminants. We demonstrate our system in several real-world indoor and outdoor settings and describe proof-of-concept applications enabled by our system that we hope will provide a foundation for new interactions in computer aided design, animation control and augmented reality.by Robert Yuanbo Wang.Ph.D

Human perception capabilities for socially intelligent domestic service robots

The daily living activities for an increasing number of frail elderly people represent a continuous struggle both for them as well as for their extended families. These people have difficulties coping at home alone but are still sufficiently fit not to need the round-the-clock care provided by a nursing home. Their struggle can be alleviated by the deployment of a mechanical helper in their home, i.e. a service robot that can execute a range of simple object manipulation tasks. Such a robotic application promises to extend the period of independent home living for elderly people, while providing them with a better quality of life. However, despite the recent technological advances in robotics, there are still some remaining challenges, mainly related to the human factors. Arguably, the lack of consistently dependable human detection, localisation, position and pose tracking information and insufficiently refined processing of sensor information makes the close range physical interaction between a robot and a human a high-risk task. The work described in this thesis addresses the deficiencies in the processing of the human information of today’s service robots. This is achieved through proposing a new paradigm for the robot’s situational awareness in regard to people as well as a collection of methods and techniques, operating at the lower levels of the paradigm, i.e. perception of new human information. The collection includes methods for obtaining and processing of information about the presence, location and body pose of the people. In addition to the availability of reliable human perception information, the integration between the separate levels of paradigm is considered to be a critically important factor for achieving the human-aware control of the robot. Improving the cognition, judgment and decision making action links between the paradigm’s layers leads to enhanced capability of the robot to engage in a natural and more meaningful interaction with people and, therefore, to a more enjoyable user experience. Therefore, the proposed paradigm and methodology are envisioned to contribute to making the prolonged assisted living of elderly people at home a more feasible and realistic task. In particular, this thesis proposes a set of methods for human presence detection, localisation and body pose tracking that are operating on the perception level of the paradigm. Also, the problem of having only limited visibility of a person from the on-board sensors of the robot is addressed by the proposed classifier fusion method that combines information from several types of sensors. A method for improved real-time human body pose tracking is also investigated. Additionally, a method for estimation of the multiple human tracks from noisy detections, as well as analysis of the computed human tracks for cognition about the social interactions within the social group, operating at the comprehension level of the robot’s situational awareness paradigm, is proposed. Finally, at the human-aware planning layer, a method that utilises the human related information, generated by the perception and comprehension layers to compute a minimally intrusive navigation path to a target person within a human group, is proposed. This method demonstrates how the improved human perception capabilities of the robot, through its judgement activity, ii ABSTRACT can be utilised by the highest level of the paradigm, i.e. the decision making layer, to achieve user friendly human-robot interactions. Overall, the research presented in this work, drawing on recent innovation in statistical learning, data fusion and optimisation methods, improves the overall situational awareness of the robot in regard to people with the main focus placed on human sensing capabilities of service robots. The improved overall situational awareness of the robot regarding people, as defined by the proposed paradigm, enables more meaningful human-robot interactions