A point-and-click interface for the real world: laser designation of objects for mobile manipulation
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Toward Human Arm Attention and Recognition
Neural Information Processing
Constrained optimization for human pose estimation from depth sequences
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Gradient-enhanced particle filter for vision-based motion capture
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Spatio-temporal 3D pose estimation of objects in stereo images
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Kinematic self retargeting: A framework for human pose estimation
Computer Vision and Image Understanding
A multiple camera system with real-time volume reconstruction for articulated skeleton pose tracking
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Towards human motion capture from a camera mounted on a mobile robot
Image and Vision Computing
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Unscented kalman filtering for articulated human tracking
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Data-driven importance distributions for articulated tracking
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Spatiotemporal analysis of human activities for biometric authentication
Computer Vision and Image Understanding
Bayesian 3d human body pose tracking from depth image sequences
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
3D body pose estimation using an adaptive person model for articulated ICP
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
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We propose a novel method for tracking an articulated model in a 3D-point cloud. The tracking problem is formulated as the registration of two point sets, one of them parameterised by the model's state vector and the other acquired from a 3D-sensor system. Finding the correct parameter vector is posed as a linear estimation problem, which is solved by means of a scaled unscented Kalman filter. Our method draws on concepts from the widely used iterative closest point registration algorithm (ICP), basing the measurement model on point correspondences established between the synthesised model point cloud and the measured 3D-data. We apply the algorithm to kinematically track a model of the human upper body on a point cloud obtained through stereo image processing from one or more stereo cameras. We determine torso position and orientation as well as joint angles of shoulders and elbows. The algorithm has been successfully tested on thousands of frames of real image data. Challenging sequences of several minutes length where tracked correctly. Complete processing time remains below one second per frame.