Stochastic Method for Skeleton Based Human Action Diagnostics

DOI

10.2991/aisr.k.201029.024How to use a DOI?

Keywords

behavior modeling, human action recognition, Hidden Markov Models, intermediate state sequences, skeleton based actions detection

Abstract

In recent years, modeling of human actions and activity patterns for recognition or detection of the special situation has attracted a significant research interest. We present our approach for abnormal human action recognition as a sequence of intermediate states. We propose to decompose each action into a sequence of discrete intermediate states and to present state transitions as a stochastic process. Each state is described with the joint locations of a human skeleton. Actions are described with Hidden Markov Model based on the found states and its interconnections. As a result, we combine our stochastic model of human actions with intermediate states described via skeleton joints. Convolutional Neural Network is employed to learn skeleton features for intermediate state recognition. Viterbi algorithm is employed to find model parameters. We implemented proposed methods in a framework for human abnormal action recognition and tested our approach on two samples: MPII Human Pose Dataset and exam footages.

Copyright

© 2020, 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  - Yury Egorov
AU  - Irina Zakharova
AU  - Andrew Filitsin
AU  - Alexandr Gasanov
PY  - 2020
DA  - 2020/11/10
TI  - Stochastic Method for Skeleton Based Human Action Diagnostics
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 121
EP  - 126
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.024
DO  - 10.2991/aisr.k.201029.024
ID  - Egorov2020
ER  -