Geometric computing with Clifford algebras: theoretical foundations and applications in computer vision and robotics
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
International Journal of Computer Vision
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Nonparametric density estimation for human pose tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Augmenting hand animation with three-dimensional secondary motion
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Three-dimensional proxies for hand-drawn characters
ACM Transactions on Graphics (TOG)
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In order to overcome typical problems in markerless motion capture from video, such as ambiguities, noise, and occlusions, many techniques reduce the high dimensional search space by integration of prior information about the movement pattern or scene. In this work, we present an approach in which geometric prior information about the floor location is integrated in the pose tracking process. We penalize poses in which body parts intersect the ground plane by employing soft constraints in the pose estimation framework. Experiments with rigid objects and the HumanEVA-II benchmark show that tracking is remarkably stabilized.