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
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
3-D Articulated Pose Tracking for Untethered Diectic Reference
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
ICML '05 Proceedings of the 22nd international conference on Machine learning
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Self-Organizing Maps for Pose Estimation with a Time-of-Flight Camera
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
A multi-robot exploration algorithm based on a static Bluetooth communication chain
Robotics and Autonomous Systems
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns
International Journal of Computer Vision
Performance capture of interacting characters with handheld kinects
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A survey of human motion analysis using depth imagery
Pattern Recognition Letters
Detecting interaction above digital tabletops using a single depth camera
Machine Vision and Applications
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In this article, we present an approach for the fusion of 2d and 3d measurements for model-based person tracking, also known as Human Motion Capture. The applied body model is defined geometrically with generalized cylinders, and is set up hierarchically with connecting joints of different types. The joint model can be parameterized to control the degrees of freedom, adhesion and stiffness. This results in an articulated body model with constrained kinematic degrees of freedom. The fusion approach incorporates this model knowledge together with the measurements, and tracks the target body iteratively with an extended Iterative Closest Point (ICP) approach. Generally, the ICP is based on the concept of correspondences between measurements and model, which is normally exploited to incorporate 3d point cloud measurements. The concept has been generalized to represent and incorporate also 2d image space features. Together with the 3D point cloud from a 3d time-of-flight (ToF) camera, arbitrary features, derived from 2D camera images, are used in the fusion algorithm for tracking of the body. This gives complementary information about the tracked body, enabling not only tracking of depth motions but also turning movements of the human body, which is normally a hard problem for markerless human motion capture systems. The resulting tracking system, named VooDoo is used to track humans in a Human-Robot Interaction (HRI) context. We only rely on sensors on board the robot, i.e. the color camera, the ToF camera and a laser range finder. The system runs in realtime (~20 Hz) and is able to robustly track a human in the vicinity of the robot.