Parameterized Modeling and Recognition of Activities
Abstract
In this paper we consider a class of human activities-atomic activities- which can be represented as a set of measurements over a finite temporal window (e.g., the motion of human body parts during a walking cycle) and which has a relatively small space of variations in performance. A new approach for modeling and recognition of atomic activities that employs principal component analysis and analytical global transformations, is proposed. The modeling of sets of exemplar instances of activities that are similar in duration and involve similar body part motions is achieved by parameterizing their representation using principal component analysis. The recognition of variants of modeled activities is achieved by searching the space of admissible parameterized transformations that these activities can undergo. This formulation iteratively refines the recognition of the class to which the observed activity belongs and the transformation parameters that relate it to the model in its class. W...
Cite
Text
Yacoob and Black. "Parameterized Modeling and Recognition of Activities." IEEE/CVF International Conference on Computer Vision, 1998. doi:10.1109/ICCV.1998.710709Markdown
[Yacoob and Black. "Parameterized Modeling and Recognition of Activities." IEEE/CVF International Conference on Computer Vision, 1998.](https://mlanthology.org/iccv/1998/yacoob1998iccv-parameterized/) doi:10.1109/ICCV.1998.710709BibTeX
@inproceedings{yacoob1998iccv-parameterized,
title = {{Parameterized Modeling and Recognition of Activities}},
author = {Yacoob, Yaser and Black, Michael J.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1998},
pages = {120-127},
doi = {10.1109/ICCV.1998.710709},
url = {https://mlanthology.org/iccv/1998/yacoob1998iccv-parameterized/}
}