Gesture Recognition Using Template Based Random Forest Classifiers
Abstract
This paper presents a framework for spotting and recognizing continuous human gestures. Skeleton based features are extracted from normalized human body coordinates to represent gestures. These features are then used to construct spatio-temporal template based Random Decision Forest models. Finally, predictions from different models are fused at decision-level to improve overall recognition performance. Our method has shown competitive results on the ChaLearn 2014 Looking at People: Gesture Recognition dataset. Trained on a dataset of 20 gesture vocabulary and 7754 gesture samples, our method achieved a Jaccard Index of $0.74663$ on the test set, reaching 7th place among contenders. Among methods that exclusively used skeleton based features, our method obtained the highest recognition performance.
Cite
Text
Camgöz et al. "Gesture Recognition Using Template Based Random Forest Classifiers." European Conference on Computer Vision Workshops, 2014. doi:10.1007/978-3-319-16178-5_41Markdown
[Camgöz et al. "Gesture Recognition Using Template Based Random Forest Classifiers." European Conference on Computer Vision Workshops, 2014.](https://mlanthology.org/eccvw/2014/camgoz2014eccvw-gesture/) doi:10.1007/978-3-319-16178-5_41BibTeX
@inproceedings{camgoz2014eccvw-gesture,
title = {{Gesture Recognition Using Template Based Random Forest Classifiers}},
author = {Camgöz, Necati Cihan and Kindiroglu, Ahmet Alp and Akarun, Lale},
booktitle = {European Conference on Computer Vision Workshops},
year = {2014},
pages = {579-594},
doi = {10.1007/978-3-319-16178-5_41},
url = {https://mlanthology.org/eccvw/2014/camgoz2014eccvw-gesture/}
}