Detecting unusual activity in video

  • Authors:

  • Hua Zhong;Jianbo Shi;Mirkó Visontai

  • Affiliations:

  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA;Computer and Information Science, University of Pennsylvania, Philadelphia, PA;Computer and Information Science, University of Pennsylvania, Philadelphia, PA

  • Venue:
  • CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2004

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Abstract

We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype-segment co-occurrence matrix is computed. Motivated by a similar problem in document-keyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.