Earticle

초록

영어

Crowd motion analysis in public places is an important research subject in the monitoring field. This paper proposes an approach for detecting abnormal crowd motion using Hidden Conditional Random Fields Model (HCRF). This approach derives variations of motion patterns from direction distribution of the crowd motion obtained by the optical flow and these variations are encoded with HCRF to allow for the detection of abnormal crowd motion. Modeling the temporal neighborhood relations in a video sequence based on HCRF can incorporate hidden states and label the video depending on long range observations. The experimental results show that this proposed algorithm can achieve better results than HMM and CRF.

목차

Abstract
 1. Introduction
 2. Motion Feature Extraction
 3. HCRF Model
 4. Experimental Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Dongping Zhang [ College of Information Engineering China Jiliang University, Hangzhou 310018, China ]
• Kaihang Xu [ College of Information Engineering China Jiliang University, Hangzhou 310018, China ]
• Yafei Lu [ College of Information Engineering China Jiliang University, Hangzhou 310018, China ]
• Chen Pan [ College of Information Engineering China Jiliang University, Hangzhou 310018, China ]
• Huailiang Peng [ College of Information Engineering China Jiliang University, Hangzhou 310018, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605