Human Action Recognition Based on Deep Images and Dense Trajectories

DOI

10.2991/ncce-18.2018.169How to use a DOI?

Keywords

Human Action Recognition; Depth Map; Dense Trajectories; SVM.

Abstract

The main content of this paper is to implement a human action recognition method based on depth image and dense trajectories. Firstly, the binocular RGB camera is used to collect images, and then the depth image is obtained through stereo matching algorithm. We use depth images to extract human action sequences. Then we choose dense optical flow field to calculate the trajectory of human sequence. After that, we compare the HOG method with MBH method and HOF method. Finally, we use SVM to complete the recognition of human actions.

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  - Xiaopeng Cui
AU  - Binwen Fan
AU  - Jingyu Yi
PY  - 2018/05
DA  - 2018/05
TI  - Human Action Recognition Based on Deep Images and Dense Trajectories
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 1012
EP  - 1018
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.169
DO  - 10.2991/ncce-18.2018.169
ID  - Cui2018/05
ER  -