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Videos
Note: In all videos, the configurations/poses are not post-processed, i.e. neither filtered nor smoothed.
Markerless Human Motion Capture
Markerless human motion capture is a prerequisite for imitation learning as well as imitation learning. Our research focusses on real-time stereo-based methods that utilize the stereo camera system of typical humanoid robot heads, having a baseline comparable to human eye distance. As statistical framework a particle filter is used.
Video: Tracking result computed by the human motion capture system. [wmv] [mp4]
Object Recognition and Pose Estimation
In robotics, accurate pose estimation of recognized objects is a prerequisite for object manipulation, grasp planning, and motion planning. Our research focusses on developing real-time methods for object recognition and in particular accurate pose estimation for these applications, using the stereo camera system of typical humanoid robot heads with a baseline comparable to human eye distance.
Videos of two systems for two different kinds of objects are presented: textured objects and single-colored objects. For the first class of objects, an approach based on local features is used, incorporating our developed high-speed features as a combination of the Harris corner detector and the SIFT descriptor. The developed approach for the second class of objects combines appearance-based view matching, stereo triangulation and 3D object model information.
Videos
- 6-DoF tracking of a single-colored object. [wmv] [mp4]
- Feature correspondences and 2D localization result throughout tracking of a textured object. [wmv] [mp4]
- 6-DoF tracking of textured objects. [wmv] [mp4]
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