Discovering recurrent events in video using unsupervised methods

Milind R. Naphade, Thomas S Huang

Research output: Contribution to conferencePaper

Abstract

Production videos such as news, sports and movies have a definitive structure that involves short term interaction as well as long term correlation. This structure in video can be captured by models that take into consideration the short term statistics as well as long term recurrence. We investigate the application of probabilistic models that capture this structure. The novel approach is to characterize the short term events in video by models that can account for temporal support in terms of piece-wise stationary signals with transitions. These short term events can then be embedded within another temporal model that accounts for transitions between these event and thus characterizes long term history. This also leads to the detection of recurring events in video using a monolithic model. The proposed approach is an unsupervised algorithm for event detection and it can be used for summarization, similarity based matching and enhanced browsing.

Original languageEnglish (US)
StatePublished - Jan 1 2002
EventInternational Conference on Image Processing (ICIP'02) - Rochester, NY, United States
Duration: Sep 22 2002Sep 25 2002

Other

OtherInternational Conference on Image Processing (ICIP'02)
CountryUnited States
CityRochester, NY
Period9/22/029/25/02

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Naphade, M. R., & Huang, T. S. (2002). Discovering recurrent events in video using unsupervised methods. Paper presented at International Conference on Image Processing (ICIP'02), Rochester, NY, United States.