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Journal Article
Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs
- Authors
- Shian-Ru Ke, Hoang Le Uyen Thuc, Jenq-Neng Hwang, Jang-Hee Yoo, Kyoung-Ho Choi
- Issue Date
- 2014-08
- Citation
- ETRI Journal, v.36, no.4, pp.662-672
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- 3D Coordinates, Baum-welch, Feature Vector, Graphical Model, Hidden markov model(HMM), Human action recognition, Human modeling, K-Means Clustering, Modeling techniques, Relational Features, Video sequences