Construction and animation of anatomically based human hand models
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
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
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
Pose estimation and tracking using multivariate regression
Pattern Recognition Letters
Real-time hand-tracking with a color glove
ACM SIGGRAPH 2009 papers
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
Markerless and efficient 26-DOF hand pose recovery
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
A Framework for 3D Model-Based Visual Tracking Using a GPU-Accelerated Particle Filter
IEEE Transactions on Visualization and Computer Graphics
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human Motion Tracking by Temporal-Spatial Local Gaussian Process Experts
IEEE Transactions on Image Processing
Tracking the articulated motion of two strongly interacting hands
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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This paper presents a novel solution to the problem of tracking the 3D position, orientation and full articulation of a human hand from single depth images. We choose the model-based approach and treat the tracking task as an optimization problem. A new objective function based on depth information is presented to quantify the discrepancy between the appearance of hypothesized instances of a hand model and actual hand observations. Sequential Particle Swarm Optimization method is proposed to minimize the objective function for sequential optimization. An semi-automatic hand location method is adopted to predict hand region for sequential tracking. A GPU-based implementation of the proposed method is well designed to address the computational intensity. Extensive experimental results demonstrate qualitatively and quantitatively that tracking of an articulated hand can be achieved in real-time.