Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition
Abstract
We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions. We also show how the descriptors can be computed quickly through the use of integral video representations. Experiments on the UCF sport, CK+ facial expression and Cambridge hand gesture datasets indicate superior performance of the proposed method compared to several recent state-of-the-art techniques. The proposed method is robust and does not require additional processing of the videos, such as foreground detection, interest-point detection or tracking.
Cite
Text
Sanin et al. "Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition." IEEE/CVF Winter Conference on Applications of Computer Vision, 2013. doi:10.1109/WACV.2013.6475006Markdown
[Sanin et al. "Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition." IEEE/CVF Winter Conference on Applications of Computer Vision, 2013.](https://mlanthology.org/wacv/2013/sanin2013wacv-spatio/) doi:10.1109/WACV.2013.6475006BibTeX
@inproceedings{sanin2013wacv-spatio,
title = {{Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition}},
author = {Sanin, Andres and Sanderson, Conrad and Harandi, Mehrtash Tafazzoli and Lovell, Brian C.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2013},
pages = {103-110},
doi = {10.1109/WACV.2013.6475006},
url = {https://mlanthology.org/wacv/2013/sanin2013wacv-spatio/}
}