15,445 research outputs found
Physical simulation for monocular 3D model based tracking
The problem of model-based object tracking in three dimensions is addressed. Most previous work on tracking assumes simple motion models, and consequently tracking typically fails in a variety of situations. Our insight is that incorporating physics models of object behaviour improves tracking performance in these cases. In particular it allows us to handle tracking in the face of rigid body interactions where there is also occlusion and fast object motion. We show how to incorporate rigid body physics simulation into a particle filter. We present two methods for this based on pose and force noise. The improvements are tested on four videos of a robot pushing an object, and results indicate that our approach performs considerably better than a plain particle filter tracker, with the force noise method producing the best results over the range of test videos
Machine Analysis of Facial Expressions
No abstract
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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
Efficient illumination independent appearance-based face tracking
One of the major challenges that visual tracking algorithms face nowadays is being
able to cope with changes in the appearance of the target during tracking. Linear
subspace models have been extensively studied and are possibly the most popular
way of modelling target appearance. We introduce a linear subspace representation
in which the appearance of a face is represented by the addition of two approxi-
mately independent linear subspaces modelling facial expressions and illumination
respectively. This model is more compact than previous bilinear or multilinear ap-
proaches. The independence assumption notably simplifies system training. We only
require two image sequences. One facial expression is subject to all possible illumina-
tions in one sequence and the face adopts all facial expressions under one particular
illumination in the other. This simple model enables us to train the system with
no manual intervention. We also revisit the problem of efficiently fitting a linear
subspace-based model to a target image and introduce an additive procedure for
solving this problem. We prove that Matthews and Baker’s Inverse Compositional
Approach makes a smoothness assumption on the subspace basis that is equiva-
lent to Hager and Belhumeur’s, which worsens convergence. Our approach differs
from Hager and Belhumeur’s additive and Matthews and Baker’s compositional ap-
proaches in that we make no smoothness assumptions on the subspace basis. In the
experiments conducted we show that the model introduced accurately represents
the appearance variations caused by illumination changes and facial expressions.
We also verify experimentally that our fitting procedure is more accurate and has
better convergence rate than the other related approaches, albeit at the expense of
a slight increase in computational cost. Our approach can be used for tracking a
human face at standard video frame rates on an average personal computer
Gravity optimised particle filter for hand tracking
This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles
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