Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs
Shian-Ru Ke, Hoang Le Uyen Thuc, Jenq–Neng Hwang, Jang‐Hee Yoo, Kyoung-Ho Choi · ETRI Journal · 2014
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.