A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Sensor Modeling, Probabilistic Hypothesis Generation, and Robust Localization for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraining Human Body Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Full Body Tracking from Multiple Views Using Stochastic Sampling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
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
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
International Journal of Computer Vision
Learning to interpret pointing gestures with a time-of-flight camera
Proceedings of the 6th international conference on Human-robot interaction
ArtSurf: a method for deformable partial matching of protein small-molecule binding sites
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Simultaneous shape and pose adaption of articulated models using linear optimization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Human pose estimation from depth image using visibility estimation and key points
DHM'13 Proceedings of the 4th international conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: human body modeling and ergonomics - Volume Part II
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The perception of persons is an important capability of today's robots that work closely together with humans. An operator may use, for example, gestures to refer to an object in the environment. In order to perceive such gestures, the robot has to estimate the body pose of the operator. We focus on the marker-less motion capture of a human body by means of an Iterative Closest Point (ICP) algorithm for articulated structures. An articulated upper body model is aligned with the depth measurements of an RGB-D camera. Due to the variability of the human body, we propose an adaptive body model that is aligned within the sensor data and iteratively adjusted to the person's body dimensions. Additionally, we preserve consistency with respect to self-collisions. Besides that, we use an inverse data assignment, that is particularly utile for articulated models. Experiments with measurements of a Microsoft Kinect camera show the advantage of the approach compared to the standard articulated ICP algorithm in terms of the root mean squared (RMS) error and the number of iterations the algorithm needs to converge. In addition, we show that our consistency checks enable to recover from situations where the standard algorithm fails.