The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La recherche sur la détection et la reconnaissance de l'action humaine (HADR) basée sur des capteurs inertiels est un nouveau domaine de l'apprentissage automatique. Nous proposons un nouveau cadre de réseaux neutres convolutionnels à intervalles basé sur une séquence temporelle pour HADR en combinant un générateur de propositions d'intervalles intéressant et un classificateur basé sur des intervalles. Les expériences démontrent les bonnes performances de notre méthode.
Zhendong ZHUANG
South China University of Technology
Yang XUE
South China University of Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Zhendong ZHUANG, Yang XUE, "TS-ICNN: Time Sequence-Based Interval Convolutional Neural Networks for Human Action Detection and Recognition" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 10, pp. 2534-2538, October 2018, doi: 10.1587/transinf.2018EDL8046.
Abstract: The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8046/_p
Copier
@ARTICLE{e101-d_10_2534,
author={Zhendong ZHUANG, Yang XUE, },
journal={IEICE TRANSACTIONS on Information},
title={TS-ICNN: Time Sequence-Based Interval Convolutional Neural Networks for Human Action Detection and Recognition},
year={2018},
volume={E101-D},
number={10},
pages={2534-2538},
abstract={The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.},
keywords={},
doi={10.1587/transinf.2018EDL8046},
ISSN={1745-1361},
month={October},}
Copier
TY - JOUR
TI - TS-ICNN: Time Sequence-Based Interval Convolutional Neural Networks for Human Action Detection and Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2534
EP - 2538
AU - Zhendong ZHUANG
AU - Yang XUE
PY - 2018
DO - 10.1587/transinf.2018EDL8046
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2018
AB - The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.
ER -