A Revisit to Human Action Recognition from Depth Sequences: Guided SVM-Sampling for Joint Selection
Abstract
This paper revisits the problem of human action recognition from skeleton joint locations, and analyses the tradeoff of sampling the joint space with respect to the recognition performance and computational complexity. The provided insights led to the design of a new algorithm for automatically selecting the most appropriate set of joints for each action. During the training stage, the approach applies a guided joint sampling strategy for learning different SVM classifiers, selecting the classifier that maximizes confidence and ambiguity metrics. Experimental results on three action datasets show that pre-selecting the most varying skeleton joints for each action dramatically reduces the computational complexity while keeping competitive recognition rates.
Cite
Text
Antunes et al. "A Revisit to Human Action Recognition from Depth Sequences: Guided SVM-Sampling for Joint Selection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477582Markdown
[Antunes et al. "A Revisit to Human Action Recognition from Depth Sequences: Guided SVM-Sampling for Joint Selection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/antunes2016wacv-revisit/) doi:10.1109/WACV.2016.7477582BibTeX
@inproceedings{antunes2016wacv-revisit,
title = {{A Revisit to Human Action Recognition from Depth Sequences: Guided SVM-Sampling for Joint Selection}},
author = {Antunes, Michel and Aouada, Djamila and Ottersten, Björn E.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-8},
doi = {10.1109/WACV.2016.7477582},
url = {https://mlanthology.org/wacv/2016/antunes2016wacv-revisit/}
}