We extract and initialize kinematic models from monocular visual data from the ground up without any manual initialization, adaptation or prior model knowledge.
Visual analysis, classification and tracking of articulated motion is challenging due to the difficulties involved in separating noise and spurious variability caused by appearance, size and view point fluctuations from the task-relevant variations. By incorporating powerful domain knowledge, model based approaches are able to overcome this problem to a great extent and are actively explored by many researchers. However, model acquisition, initialization and adaptation are still relatively underinvestigated problems. In this work we show how kinematic structure can be inferred from monocular views without making any a priori assumptions about the scene except that it consists of piecewise rigid segments constrained by jointed motion. The efficacy of the method is demonstrated on synthetic as well as natural image sequences.

@inproceedings{krahnstoever01automatic,

author={N. Krahnstoever and M. Yeasin and R. Sharma},
booktitle={Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2001), Technical Sketches, Kauai Marriott, Hawaii},
month={December},
title={Automatic Acquisition and Initialization of Kinematic Models},
year={2001}

}