Research of Action Recognition Methods Based on RGB+D Videos

DOI

10.2991/icaita-18.2018.3How to use a DOI?

Keywords

action recognition; RGB+D; TSN; RNN

Abstract

In order to solve the problem on making full use of RGB+D dataset that includes RGB data, 3D skeletal data, depth map sequences and infrared videos, this paper proposes an action recognition method of RGB+D videos that merges a multi-layer recurrent neural network and two-stream convolutional networks, combining RGB information and joints information together. Simulation results show that the multi-layer recurrent network proposed in this paper has better performance than other recurrent networks when dealing with the skeletal data. Moreover, by combining it with the spatial network or temporal network through nonlinear weighted score fusion, the recognition accuracy is further improved. The cross-view action recognition accuracy is improved to be 0.79%, 5.6%, 20.62% and 23.65% higher than the original method, respectively by using the multi-layer network alone, combining the multi-layer network and spatial network, combining the multi-layer network and temp-oral network, and combining three networks together.

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  - Zhongyin Huang
AU  - Wei Chen
PY  - 2018/03
DA  - 2018/03
TI  - Research of Action Recognition Methods Based on RGB+D Videos
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 9
EP  - 12
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.3
DO  - 10.2991/icaita-18.2018.3
ID  - Huang2018/03
ER  -