Human Action Recognition based on Human skeleton Hu invariant moments combined with human geometrical characteristics

DOI

10.2991/fmsmt-17.2017.317How to use a DOI?

Keywords

Human action recognition, Moving object detection, Geometric characteristics of the human body, Human skeleton Hu invariant moments, K-nearest neighbor.

Abstract

Human action recognition is a highly active research topic in the field of video surveillance, human computer interaction and other fields. Due to the huge amount of computation, many existence methods fail in real-time applications. In this paper, we proposed a human action recognition method based on human skeleton Hu invariant moments combined with human geometrical characteristics. Firstly, foreground is extracted by the inter-frame difference and background subtraction. Secondly, human skeleton Hu invariant moments, the minimum bounding rectangle aspect ratio, rectangularity and circularity are calculated. Finally, human actions are recognized by the K-nearest neighbor. Experimental results in Weizmann action datasets show that this method can accurately identify human actions and has good real-time performance. The proposed method can be applied to real-time intelligent video surveillance with high accuracy.

Copyright

© 2017, 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  - Qing Ye
AU  - Xinran Guo
AU  - Yongmei Zhang
PY  - 2017/04
DA  - 2017/04
TI  - Human Action Recognition based on Human skeleton Hu invariant moments combined with human geometrical characteristics
BT  - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017)
PB  - Atlantis Press
SP  - 1628
EP  - 1632
SN  - 2352-5401
UR  - https://doi.org/10.2991/fmsmt-17.2017.317
DO  - 10.2991/fmsmt-17.2017.317
ID  - Ye2017/04
ER  -