상세정보
학술지
Human Action Recognition Based on 3D Human Modeling and Cyclic HMMs
- 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)
- 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