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학술지 Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs
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저자
Shian-Ru Ke, Hoang Le Uyen Thuc, Jenq-Neng Hwang, 유장희, 최경호
발행일
201408
출처
ETRI Journal, v.36 no.4, pp.662-672
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.14.0113.0647
협약과제
13VS1100, 사람에 의한 안전위협의 실시간 인지를 위한 능동형 영상보안 서비스용 원거리 (CCTV 주간환경 5m이상) 사람 식별 및 검색 원천기술 개발, 유장희
초록
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.
키워드
3D modeling, Geometrical relational features, Hidden Markov model, Human action recognition
KSP 제안 키워드
3D Coordinates, 3D Modeling, Baum-Welch, Feature Vector, Human modeling, Modeling techniques, Relational Features, continuous action recognition, dimensionality reduction, estimation algorithm, graphical model