60 research outputs found
Single camera pose estimation using Bayesian filtering and Kinect motion priors
Traditional approaches to upper body pose estimation using monocular vision
rely on complex body models and a large variety of geometric constraints. We
argue that this is not ideal and somewhat inelegant as it results in large
processing burdens, and instead attempt to incorporate these constraints
through priors obtained directly from training data. A prior distribution
covering the probability of a human pose occurring is used to incorporate
likely human poses. This distribution is obtained offline, by fitting a
Gaussian mixture model to a large dataset of recorded human body poses, tracked
using a Kinect sensor. We combine this prior information with a random walk
transition model to obtain an upper body model, suitable for use within a
recursive Bayesian filtering framework. Our model can be viewed as a mixture of
discrete Ornstein-Uhlenbeck processes, in that states behave as random walks,
but drift towards a set of typically observed poses. This model is combined
with measurements of the human head and hand positions, using recursive
Bayesian estimation to incorporate temporal information. Measurements are
obtained using face detection and a simple skin colour hand detector, trained
using the detected face. The suggested model is designed with analytical
tractability in mind and we show that the pose tracking can be
Rao-Blackwellised using the mixture Kalman filter, allowing for computational
efficiency while still incorporating bio-mechanical properties of the upper
body. In addition, the use of the proposed upper body model allows reliable
three-dimensional pose estimates to be obtained indirectly for a number of
joints that are often difficult to detect using traditional object recognition
strategies. Comparisons with Kinect sensor results and the state of the art in
2D pose estimation highlight the efficacy of the proposed approach.Comment: 25 pages, Technical report, related to Burke and Lasenby, AMDO 2014
conference paper. Code sample: https://github.com/mgb45/SignerBodyPose Video:
https://www.youtube.com/watch?v=dJMTSo7-uF
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications
Improving irradiance forecasting is critical to further increase the share of
solar in the energy mix. On a short time scale, fish-eye cameras on the ground
are used to capture cloud displacements causing the local variability of the
electricity production. As most of the solar radiation comes directly from the
Sun, current forecasting approaches use its position in the image as a
reference to interpret the cloud cover dynamics. However, existing Sun tracking
methods rely on external data and a calibration of the camera, which requires
access to the device. To address these limitations, this study introduces an
image-based Sun tracking algorithm to localise the Sun in the image when it is
visible and interpolate its daily trajectory from past observations. We
validate the method on a set of sky images collected over a year at SIRTA's
lab. Experimental results show that the proposed method provides robust smooth
Sun trajectories with a mean absolute error below 1% of the image size.Comment: Accepted as a workshop paper at NeurIPS 202
Singularities of serial robots: identification and distance computation using geometric algebra
The singularities of serial robotic manipulators are those configurations in which the robot loses the ability to move in at least one direction. Hence, their identification is fundamental to enhance the performance of current control and motion planning strategies. While classical approaches entail the computation of the determinant of either a 6×n or n×n matrix for an n-degrees-of-freedom serial robot, this work addresses a novel singularity identification method based on modelling the twists defined by the joint axes of the robot as vectors of the six-dimensional and three-dimensional geometric algebras. In particular, it consists of identifying which configurations cause the exterior product of these twists to vanish. In addition, since rotors represent rotations in geometric algebra, once these singularities have been identified, a distance function is defined in the configuration space C , such that its restriction to the set of singular configurations S allows us to compute the distance of any configuration to a given singularity. This distance function is used to enhance how the singularities are handled in three different scenarios, namely, motion planning, motion control and bilateral teleoperation.Peer ReviewedPostprint (published version
Estimating missing marker positions using low dimensional Kalman smoothing.
Motion capture is frequently used for studies in biomechanics, and has proved particularly useful in understanding human motion. Unfortunately, motion capture approaches often fail when markers are occluded or missing and a mechanism by which the position of missing markers can be estimated is highly desirable. Of particular interest is the problem of estimating missing marker positions when no prior knowledge of marker placement is known. Existing approaches to marker completion in this scenario can be broadly divided into tracking approaches using dynamical modelling, and low rank matrix completion. This paper shows that these approaches can be combined to provide a marker completion algorithm that not only outperforms its respective components, but also solves the problem of incremental position error typically associated with tracking approaches
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