Acquisition of Articulated Human Body Models Using Multiple Cameras
Motion capture is an important application in different areas such as biomechanics, computer animation, and human-computer interaction. Current motion capture methods typically use human body models in order to guide pose estimation and tracking. We model the human body as a set of tapered super-quadrics connected in an articulated structure and propose an algorithm to automatically estimate the parameters of the model using video sequences obtained from multiple calibrated cameras. Our method is based on the fact that the human body is constructed of several articulated chains that can be visualised as essentially 1-D segments embedded in 3-D space and connected at specific joint locations. The proposed method first computes a voxel representation from the images and maps the voxels to a high dimensional space in order to extract the 1-D structure. A bottom-up approach is then suggested in order to build a parametric (spline-based) representation of a general articulated body in the high dimensional space followed by a top-down probabilistic approach that registers the segments to the known human body model. We then present an algorithm to estimate the parameters of our model using the segmented and registered voxels.
KeywordsJoint Angle Coordinate Frame Motion Capture High Dimensional Space Fill Ratio
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- 1.Moeslund, T., Granum, E.: A survey of computer vision-based human motion capture. In: CVIU, 231–268 (2001)Google Scholar
- 2.Krahnstoever, N., Sharma, R.: Articulated models from video. In: Computer Vision and Pattern Recognition, pp. 894–901 (2004)Google Scholar
- 3.Mikic, I., Trivedi, M., Hunter, E., Cosman, P.: Human body model acquisition and tracking using voxel data. International Journal of Computer Vision 53 (2003)Google Scholar
- 4.Kakadiaris, I.A., Metaxas, D.: 3D human body model acquisition from multiple views. In: Fifth International Conference on Computer Vision, p. 618 (1995)Google Scholar
- 5.Anguelov, D., Koller, D., Pang, H., Srinivasan, P., Thrun, S.: Recovering articulated object models from 3-D range data. In: Uncertainty in Artificial Intelligence Conference (2004)Google Scholar
- 6.Cheung, K., Baker, S., Kanade, T.: Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: IEEE CVPR, pp. 77–84 (2003)Google Scholar
- 7.Chu, C.W., Jenkins, O.C., Mataric, M.J.: Markerless kinematic model and motion capture from volume sequences. In: CVPR (2), pp. 475–482 (2003)Google Scholar
- 11.Brand, M.: Charting a manifold. In: Neural Information Processing Systems (2002)Google Scholar