Visual tracking of high DOF articulated structures: an application to human hand tracking
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
3D articulated models and multiview tracking with physical forces
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Towards 3D hand tracking using a deformable model
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
Visual Hand Tracking Using Nonparametric Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
3D Distance Fields: A Survey of Techniques and Applications
IEEE Transactions on Visualization and Computer Graphics
Regression-based Hand Pose Estimation from Multiple Cameras
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
Real-time hand-tracking with a color glove
ACM SIGGRAPH 2009 papers
Combined Region and Motion-Based 3D Tracking of Rigid and Articulated Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markerless and efficient 26-DOF hand pose recovery
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Model-Based 3D Hand Pose Estimation from Monocular Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markerless motion capture of interacting characters using multi-view image segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Tracking the articulated motion of two strongly interacting hands
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hand pose estimation by combining fingertip tracking and articulated ICP
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
Video-based hand manipulation capture through composite motion control
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
HandSonor: a customizable vision-based control interface for musical expression
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization
The Visual Computer: International Journal of Computer Graphics
Hand gesture recognition with depth data
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
| Hi-index | 0.00 |
Capturing the motion of two hands interacting with an object is a very challenging task due to the large number of degrees of freedom, self-occlusions, and similarity between the fingers, even in the case of multiple cameras observing the scene. In this paper we propose to use discriminatively learned salient points on the fingers and to estimate the finger-salient point associations simultaneously with the estimation of the hand pose. We introduce a differentiable objective function that also takes edges, optical flow and collisions into account. Our qualitative and quantitative evaluations show that the proposed approach achieves very accurate results for several challenging sequences containing hands and objects in action.