Linear-Time Online Action Detection from 3D Skeletal Data Using Bags of Gesturelets
Abstract
Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is suitable for real-time applications with low latency.
Cite
Text
Meshry et al. "Linear-Time Online Action Detection from 3D Skeletal Data Using Bags of Gesturelets." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477587Markdown
[Meshry et al. "Linear-Time Online Action Detection from 3D Skeletal Data Using Bags of Gesturelets." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/meshry2016wacv-linear/) doi:10.1109/WACV.2016.7477587BibTeX
@inproceedings{meshry2016wacv-linear,
title = {{Linear-Time Online Action Detection from 3D Skeletal Data Using Bags of Gesturelets}},
author = {Meshry, Moustafa and Hussein, Mohamed E. and Torki, Marwan},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-9},
doi = {10.1109/WACV.2016.7477587},
url = {https://mlanthology.org/wacv/2016/meshry2016wacv-linear/}
}