Human Action Recognition Based on RGB-D and Local Interactive Regions Detection

DOI

10.2991/sser-18.2018.17How to use a DOI?

Keywords

Action; RGB-D; Interactive regions; Recognition

Abstract

We propose a novel method to recognize human actions by fusing information from RGB-D sensors. Human action recognition is a challenging task because of the complexity movements, the variety of actions performed by different subjects and the changes of view and illumination. We propose to detect human motion from body parts by extracting sets of spatial-temporal interest points from RGB sequence and projecting them into depth map. Then, extract local interactive regions as supplementary information for action recognition. An improve classifier based on linear SVM coupled with dynamic time warping is developed for classification. We evaluate our method on two public datasets, including MSRDailyActivity3D dataset and ReadingAct RGB-D action dataset.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Suolan Liu
AU  - Lizhi Kong
PY  - 2018/05
DA  - 2018/05
TI  - Human Action Recognition Based on RGB-D and Local Interactive Regions Detection
BT  - Proceedings of the 2018 8th International Conference on Social science and Education Research (SSER 2018)
PB  - Atlantis Press
SP  - 81
EP  - 85
SN  - 2352-5398
UR  - https://doi.org/10.2991/sser-18.2018.17
DO  - 10.2991/sser-18.2018.17
ID  - Liu2018/05
ER  -