Multiple Hypothesis Tracking for Automatic Optical Motion Capture
Abstract
We present a technique for performing the tracking stage of optical motion capture which retains, at each time frame, multiple marker association hypotheses and estimates of the subject’s position. Central to this technique are the equations for calculating the likelihood of a sequence of association hypotheses, which we develop using a Bayesian approach. The system is able to perform motion capture using fewer cameras and a lower frame rate than has been used previously, and does not require the assistance of a human operator. We conclude by demonstrating the tracker on real data and provide an example in which our technique is able to correctly determine all marker associations and standard tracking techniques fail.
Cite
Text
Ringer and Lasenby. "Multiple Hypothesis Tracking for Automatic Optical Motion Capture." European Conference on Computer Vision, 2002. doi:10.1007/3-540-47969-4_35Markdown
[Ringer and Lasenby. "Multiple Hypothesis Tracking for Automatic Optical Motion Capture." European Conference on Computer Vision, 2002.](https://mlanthology.org/eccv/2002/ringer2002eccv-multiple/) doi:10.1007/3-540-47969-4_35BibTeX
@inproceedings{ringer2002eccv-multiple,
title = {{Multiple Hypothesis Tracking for Automatic Optical Motion Capture}},
author = {Ringer, Maurice and Lasenby, Joan},
booktitle = {European Conference on Computer Vision},
year = {2002},
pages = {524-536},
doi = {10.1007/3-540-47969-4_35},
url = {https://mlanthology.org/eccv/2002/ringer2002eccv-multiple/}
}