39,095 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
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
We present a real-time approach for multi-person 3D motion capture at over 30
fps using a single RGB camera. It operates successfully in generic scenes which
may contain occlusions by objects and by other people. Our method operates in
subsequent stages. The first stage is a convolutional neural network (CNN) that
estimates 2D and 3D pose features along with identity assignments for all
visible joints of all individuals.We contribute a new architecture for this
CNN, called SelecSLS Net, that uses novel selective long and short range skip
connections to improve the information flow allowing for a drastically faster
network without compromising accuracy. In the second stage, a fully connected
neural network turns the possibly partial (on account of occlusion) 2Dpose and
3Dpose features for each subject into a complete 3Dpose estimate per
individual. The third stage applies space-time skeletal model fitting to the
predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose,
and enforce temporal coherence. Our method returns the full skeletal pose in
joint angles for each subject. This is a further key distinction from previous
work that do not produce joint angle results of a coherent skeleton in real
time for multi-person scenes. The proposed system runs on consumer hardware at
a previously unseen speed of more than 30 fps given 512x320 images as input
while achieving state-of-the-art accuracy, which we will demonstrate on a range
of challenging real-world scenes.Comment: To appear in ACM Transactions on Graphics (SIGGRAPH) 202
XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera
We present a real-time approach for multi-person 3D motion capture at over 30 fps using a single RGB camera. It operates in generic scenes and is robust to difficult occlusions both by other people and objects. Our method operates in subsequent stages. The first stage is a convolutional neural network (CNN) that estimates 2D and 3D pose features along with identity assignments for all visible joints of all individuals. We contribute a new architecture for this CNN, called SelecSLS Net, that uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy. In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual. The third stage applies space-time skeletal model fitting to the predicted 2D and 3D pose per subject to further reconcile the 2D and 3D pose, and enforce temporal coherence. Our method returns the full skeletal pose in joint angles for each subject. This is a further key distinction from previous work that neither extracted global body positions nor joint angle results of a coherent skeleton in real time for multi-person scenes. The proposed system runs on consumer hardware at a previously unseen speed of more than 30 fps given 512x320 images as input while achieving state-of-the-art accuracy, which we will demonstrate on a range of challenging real-world scenes
A practical multirobot localization system
We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems
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