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

Transition from inspiral to plunge: A complete near-extremal trajectoryand associated waveform

We extend the Ori and Thorne (OT) procedure to compute the transition from an adiabatic inspiral into a geodesic plunge for any spin. Our analysis revisits the validity of the approximations made in OT. In particular, we discuss possible effects coming from eccentricity and non-geodesic past-history of the orbital evolution. We find three different scaling regimes according to whether the mass ratio is much smaller, of the same order or much larger than the near extremal parameter describing how fast the primary black hole rotates. Eccentricity and non-geodesic past-history corrections are always sub-leading, indicating that the quasi-circular approximation applies throughout the transition regime. However, we show that the OT assumption that the energy and angular momentum evolve linearly with proper time must be modified in the near-extremal regime. Using our transition equations, we describe an algorithm to compute the full worldline in proper time for an extreme mass ratio inspiral (EMRI) and the full gravitational waveform in the high spin limit

Constraining the spin parameter of near-extremal black holes using LISA

We describe a model that generates first order adiabatic EMRI waveforms for quasi-circular equatorial inspirals of compact objects into rapidly rotating (near-extremal) black holes. Using our model, we show that LISA could measure the spin parameter of near-extremal black holes (for $a \gtrsim 0.9999$) with extraordinary precision, $\sim$ 3-4 orders of magnitude better than for moderate spins, $a \sim 0.9$. Such spin measurements would be one of the tightest measurements of an astrophysical parameter within a gravitational wave context. Our results are primarily based off a Fisher matrix analysis, but are verified using both frequentest and Bayesian techniques. We present analytical arguments that explain these high spin precision measurements. The high precision arises from the spin dependence of the radial inspiral evolution, which is dominated by geodesic properties of the secondary orbit, rather than radiation reaction. High precision measurements are only possible if we observe the exponential damping of the signal that is characteristic of the near-horizon regime of near-extremal inspirals. Our results demonstrate that, if such black holes exist, LISA would be able to successfully identify rapidly rotating black holes up to $a = 1-10^{-9}$ , far past the Thorne limit of $a = 0.998$.Comment: 31 pages, 18 figures, 1 table. Accepted for publication in Phys. Rev.

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

Clustering climate and management practices to define environmental challenges affecting gastrointestinal parasitism in Katahdin sheep

Gastrointestinal nematodes (GIN) negatively affect the performance and well-being of sheep. Due to anthelmintic resistance, GIN are difficult to control leading producers to choose breeds that can exhibit resistance to parasitism. An example is Katahdin sheep. Katahdins are raised in various climates and management systems in the United States. These environmental factors can be combined to form eco-management groupings or clusters. We hypothesized that GIN challenge varies predictably based on the characteristics of these environmental clusters. Forty Katahdin producers from across the United States were surveyed for management information, with body weights (BW), fecal egg counts (FEC), and FAMACHA scores (FAM) available from 17 of the 40 flocks. The performance data included 3,426 lambs evaluated around 90 d of age. Management and climate data were combined into clusters using multiple correspondence and principal component (PC) analysis. Performance data were aligned with their corresponding cluster. Depending on the trait, eco-management cluster, birth-rearing type, sex, and, as a covariate, dam age, were fitted as systematic effects with ANOVA. Clusters also were formed based on climate or management data alone. When compared with fitting the eco-management clusters, they defined less variation in each of the traits based on Akaike and Bayesian information criterion, and adjusted r2 values. To further examine variation defined by eco-management clusters, residuals from an ANOVA model excluding eco-management cluster were retained, and their correlation with PC loadings calculated. All PC loadings were included as potential independent variables and tested for significance using backward stepwise regression. The PC loadings with a correlation |≥0.49| explained significant variation in each trait and were included in the final models chosen; adjusted r2 values for BW, FEC, and FAM were 0.90, 0.81, and 0.97, respectively. When analyzing GIN challenge, eco-management clusters corresponding with hotter temperatures and greater rainfall, and with pasture-born lambs, suffered greater parasitism. Conversely, the eco-management clusters with lambs turned out to pasture at older ages benefited from reduced parasitism. Through the formation of eco-management clusters, an environmental variable can be defined to study interactions of genotypes to their environment, providing a potentially useful tool for identifying parasite-resistant sheep