한국정보과학회논문지:소프트웨어및응용 (Journal of KIISE:Software and Applications)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

동적 확률 모델 네트워크 기반 휴먼 상호 행동 인식

Hunan Interaction Recognition with a Network of Dynamic Probabilistic Models

  • 석흥일 (고려대학교 컴퓨터학과) ;
  • 이성환 (고려대학교 정보통신대학 컴퓨터.통신공학부)
  • 발행 : 2009.11.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 휴먼 객체들의 이동 궤적 정보를 기반으로 휴먼 상호 행동을 인식하기 위한 새로운 모델을 제안한다. 복잡한 휴먼 상호 행동들은 의미있는 작은 단위로 분할될 수 있는데 이를 '부-상호행동'이라 하며, 이들을 표현하는 모델들의 순차적 연결 또는 네트워크로 상호 행동을 모델링한다. 제안하는 모델은 서로 다른 상호 행동들에 공통적으로 나타나는 부-상호 행동들을 공유하도록 함으로써 모델의 복잡도를 낮추어 매우 효율적이다. 상호 행동 네트워크 모델의 동작 분석 및 기존 방법과의 비교 실험을 통해 제안한 방법의 우수성을 확인할 수 있었다.

In this paper, we propose a novel method for analyzing human interactions based on the walking trajectories of human subjects. Our principal assumption is that an interaction episode is composed of meaningful smaller unit interactions, which we call 'sub-interactions.' The whole interactions are represented by an ordered concatenation or a network of sub-interaction models. From the experiments, we could confirm the effectiveness and robustness of the proposed method by analyzing the inner workings of an interaction network and comparing the performance with other previous approaches.

키워드

참고문헌

  1. N. Oliver, B. Rosario, and A. Pentland, 'A Bayesian Computer Vision System for Modeling Human Interactions,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.22, no.8, pp.831-843, 2000 https://doi.org/10.1109/34.868684
  2. X. Liu and C. Chua, 'Multi-Agent Activity Recognition using Observation Decomposed Hidden Markov Models,' Image and Vision Computing, vol.24, pp.166-175, 2006 https://doi.org/10.1016/j.imavis.2005.09.024
  3. S. Hongeng, R. Nevatia, and F. Bremond, 'Videobased Event Recognition: Activity Representation and Probabilistic Recognition Methods,' Computer Vision and Image Understanding, vol.96, pp.129-162, 2004 https://doi.org/10.1016/j.cviu.2004.02.005
  4. M. Ryoo and J. Aggarwal, 'Semantic Representation and Recognition of Continued and Recursive Human Activities,' International Journal of Computer Vision, vol.82, no.1 pp.1-24, 2009 https://doi.org/10.1007/s11263-008-0181-1
  5. A. Hakeem and M. Shah, 'Learning Detection and Representation of Multi-Agent Events in Videos,' Artificial Intelligence, vol.171, pp.586-605, 2007 https://doi.org/10.1016/j.artint.2007.04.002
  6. T. Xiang and S. Gong, 'Video Behavior Profiling for Anomaly Detection,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30, no.5, pp.893-908, 2008 https://doi.org/10.1109/TPAMI.2007.70731
  7. L. Rabiner, 'A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,' Proceedings of the IEEE, vol.77, pp.257-285, 1989 https://doi.org/10.1109/5.18626
  8. C. Vogler and D. Metaxas, 'A Framework for Recognizing the Simultaneous Aspects of American Sign Language,' Computer Vision and Image Understanding, vol.81, no.3, pp.358-384, 2001 https://doi.org/10.1006/cviu.2000.0895
  9. Z. Ghahramani and M. Jordan, 'Factorial Hidden Markov Model,' Machine Learning, vol.29, no.2-3, pp.245-273, 1997 https://doi.org/10.1023/A:1007425814087
  10. K. Murphy, Dynamic Bayesian Network: Representation, Inference and Learning, Ph.D. Dissertation, University of California, Berkeley, 2002