Stochastic Method for Skeleton Based Human Action Diagnostics
- https://doi.org/10.2991/aisr.k.201029.024How to use a DOI?
- behavior modeling, human action recognition, Hidden Markov Models, intermediate state sequences, skeleton based actions detection
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/aisr.k.201029.024 ID - Egorov2020 ER -