15,296 research outputs found
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
Machine Analysis of Facial Expressions
No abstract
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
This paper presents a novel real-time method for tracking salient closed
boundaries from video image sequences. This method operates on a set of
straight line segments that are produced by line detection. The tracking scheme
is coherently integrated into a perceptual grouping framework in which the
visual tracking problem is tackled by identifying a subset of these line
segments and connecting them sequentially to form a closed boundary with the
largest saliency and a certain similarity to the previous one. Specifically, we
define a new tracking criterion which combines a grouping cost and an area
similarity constraint. The proposed criterion makes the resulting boundary
tracking more robust to local minima. To achieve real-time tracking
performance, we use Delaunay Triangulation to build a graph model with the
detected line segments and then reduce the tracking problem to finding the
optimal cycle in this graph. This is solved by our newly proposed closed
boundary candidates searching algorithm called "Bidirectional Shortest Path
(BDSP)". The efficiency and robustness of the proposed method are tested on
real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017) submission ID 103
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