976 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
Hand Pointing Detection Using Live Histogram Template of Forehead Skin
Hand pointing detection has multiple applications in many fields such as
virtual reality and control devices in smart homes. In this paper, we proposed
a novel approach to detect pointing vector in 2D space of a room. After
background subtraction, face and forehead is detected. In the second step,
forehead skin H-S plane histograms in HSV space is calculated. By using these
histogram templates of users skin, and back projection method, skin areas are
detected. The contours of hand are extracted using Freeman chain code
algorithm. Next step is finding fingertips. Points in hand contour which are
candidates for the fingertip can be found in convex defects of convex hull and
contour. We introduced a novel method for finding the fingertip based on the
special points on the contour and their relationships. Our approach detects
hand-pointing vectors in live video from a common webcam with 94%TP and 85%TN.Comment: Accepted for oral presentation in DSP201
A Multicamera System for Gesture Tracking With Three Dimensional Hand Pose Estimation
The goal of any visual tracking system is to successfully detect then follow an object of interest through a sequence of images. The difficulty of tracking an object depends on the dynamics, the motion and the characteristics of the object as well as on the environ ment. For example, tracking an articulated, self-occluding object such as a signing hand has proven to be a very difficult problem. The focus of this work is on tracking and pose estimation with applications to hand gesture interpretation. An approach that attempts to integrate the simplicity of a region tracker with single hand 3D pose estimation methods is presented. Additionally, this work delves into the pose estimation problem. This is ac complished by both analyzing hand templates composed of their morphological skeleton, and addressing the skeleton\u27s inherent instability. Ligature points along the skeleton are flagged in order to determine their effect on skeletal instabilities. Tested on real data, the analysis finds the flagging of ligature points to proportionally increase the match strength of high similarity image-template pairs by about 6%. The effectiveness of this approach is further demonstrated in a real-time multicamera hand tracking system that tracks hand gestures through three-dimensional space as well as estimate the three-dimensional pose of the hand
Hand Posture Recognition with standard webcam for Natural Interaction
This paper presents an experimental prototype designed for natural human-computer interaction in an environmental intelligence system. Using computer vision resources, it analyzes the images captured by a webcam to recognize a personâs hand movements. There is now a strong trend in interpreting these hand and body movements in general, with computer vision, which is a very attractive field of research. In this study, a mechanism for natural interaction was implemented by analyzing images captured by a webcam based on hand geometry and posture, to show its movements in our model. A camera is installed in such a manner that it can discriminate the movements a person makes using Background Subtraction. Then hands are searched for assisted by segmentation by skin color detection and a series of classifiers. Finally, the geometric characteristics of the hands are extracted to distinguish defined control action positions
Embedded Real Time Gesture Tracking
Video tracking is the process of locating a moving object (or several ones) in time using a camera. An algorithm evaluates the video frames and outputs the location of moving targets within the video frame
- âŠ