Managing Interclass Variation In Human Action Recognition

DOI

10.2991/pecteam-18.2018.10How to use a DOI?

Keywords

video processing, Feature Descriptor, Spatio-temporal, Action Recognition

Abstract

Background: Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category. Objective: This paper focuses on a specific issue related to inter-class variation in Human Action Recognition. To discriminate the human actions among the category, the poses of body parts are estimated and there by trailing its motion sporadically with geometric joints feature. Analysis: Example actions are listed to illustrate the similarity between the actions and steps to emulate the enhancement to discriminative the power of recognizing the similar actions. Conclusion: Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation.

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  - K Akila
AU  - S Chitrakala
PY  - 2018/02
DA  - 2018/02
TI  - Managing Interclass Variation In Human Action Recognition
BT  - Proceedings of the International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018)
PB  - Atlantis Press
SP  - 51
EP  - 55
SN  - 2352-5401
UR  - https://doi.org/10.2991/pecteam-18.2018.10
DO  - 10.2991/pecteam-18.2018.10
ID  - Akila2018/02
ER  -