Open AccessHuman Action Recognition Based on 3D Human Modeling and Cyclic HMMs
Author(s) -
Ke ShianRu,
Thuc Hoang Le Uyen,
Hwang JenqNeng,
Yoo JangHee,
Choi KyoungHo
Publication year - 2014
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.14.0113.0647
Subject(s) - action recognition , action (physics) , computer science , hidden markov model , artificial intelligence , pattern recognition (psychology) , physics , quantum mechanics , class (philosophy)
Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k‐means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum–Welch re‐estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.