337,682 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
Two-layer particle filter for multiple target detection and tracking
This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets
Importance sampling schemes for evidence approximation in mixture models
The marginal likelihood is a central tool for drawing Bayesian inference
about the number of components in mixture models. It is often approximated
since the exact form is unavailable. A bias in the approximation may be due to
an incomplete exploration by a simulated Markov chain (e.g., a Gibbs sequence)
of the collection of posterior modes, a phenomenon also known as lack of label
switching, as all possible label permutations must be simulated by a chain in
order to converge and hence overcome the bias. In an importance sampling
approach, imposing label switching to the importance function results in an
exponential increase of the computational cost with the number of components.
In this paper, two importance sampling schemes are proposed through choices for
the importance function; a MLE proposal and a Rao-Blackwellised importance
function. The second scheme is called dual importance sampling. We demonstrate
that this dual importance sampling is a valid estimator of the evidence and
moreover show that the statistical efficiency of estimates increases. To reduce
the induced high demand in computation, the original importance function is
approximated but a suitable approximation can produce an estimate with the same
precision and with reduced computational workload.Comment: 24 pages, 5 figure
A Bayesian view of doubly robust causal inference
In causal inference confounding may be controlled either through regression
adjustment in an outcome model, or through propensity score adjustment or
inverse probability of treatment weighting, or both. The latter approaches,
which are based on modelling of the treatment assignment mechanism and their
doubly robust extensions have been difficult to motivate using formal Bayesian
arguments, in principle, for likelihood-based inferences, the treatment
assignment model can play no part in inferences concerning the expected
outcomes if the models are assumed to be correctly specified. On the other
hand, forcing dependency between the outcome and treatment assignment models by
allowing the former to be misspecified results in loss of the balancing
property of the propensity scores and the loss of any double robustness. In
this paper, we explain in the framework of misspecified models why doubly
robust inferences cannot arise from purely likelihood-based arguments, and
demonstrate this through simulations. As an alternative to Bayesian propensity
score analysis, we propose a Bayesian posterior predictive approach for
constructing doubly robust estimation procedures. Our approach appropriately
decouples the outcome and treatment assignment models by incorporating the
inverse treatment assignment probabilities in Bayesian causal inferences as
importance sampling weights in Monte Carlo integration.Comment: Author's original version. 21 pages, including supplementary materia
Examining the Association Between Traditional and Mainstream Medicine and the Prevalence of Arthritis in the Urban Indigenous Population Living in Toronto
Background: In the Indigenous community, the prevalence of arthritis is 1.3 to 1.6 times higher than their non-Indigenous counterparts. Moreover, minimal population health information on urban Indigenous peoples is available.
Objective: To explore the relationship between the use of traditional and mainstream medicine and the prevalence of arthritis in the Indigenous population living in Toronto.
Methods: The Our Health Counts Toronto study surveyed 918 self-identified Indigenous adults using Respondent-Driven Sampling. Survey logistic regression and generalized linear mixed models were used to investigate the multivariable relationships between medication use and arthritis, including adjustments for known confounders.
Results: Compared to using neither types of medicine, use of both mainstream and traditional medicines (OR: 8.69, 95% CI: 4.06-18.59), mainstream medicine use only (6.08 2.41-15.36) and traditional medicine use only (3.86 2.63-5.67) are associated with arthritis.
Conclusion: Indigenous community members with arthritis are likely to use both traditional and mainstream medicine to manage this condition
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