Earticle

초록

영어

Abnormal event detection in nature settings is an active issue in computer vision domain. A novel unsupervised method is proposed to detect abnormal events by combining dynamic texture and sparse coding. In this method, dynamic texture is used as descriptors in a spatio-temporal manner to describe spatio-temporal volumes of events in videos. Sparse coding is utilized for reconstructing the testing data to measure its normalness. Experiments are conducted on the well known UCSD dataset and UMN dataset to demonstrate the efficiency of the proposed method. The results show that the proposed method outperforms the current state-of-the-art methods.

목차

Abstract
 1. Introduction
 2. Video Representation
  2.1. Spatio-Temporal Cuboids
  2.2. LBP-TOP Descriptor for Spatio-Temporal Cuboids
 3. Abnormal Event Detection Using Sparse Coding
  3.1 Sparse Coding
  3.2 Abnormal Event Detection
 4. Experiments and Comparisons
  4.1 UCSD Dataset and Experimental Results Analysis
  4.2 UMN Dataset and Experimental Results Analysis
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Yang Liu [ College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China ]
• Yibo Li [ College of Automation, Shenyang Aerospace University, Shenyang, China ]
• Xiaofei Ji [ College of Automation, Shenyang Aerospace University, Shenyang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605