Computational Vision Approaches for Event Modeling

Rama Chellappa, Naresh P. Cuntoor, Seong Wook Joo, V. S. Subrahmanian, Pavan Turaga

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Event modeling systems provide a semantic interpretation of sequences of pixels that are captured by a video camera. The design of a practical system has to take into account the following three main factors: low-level preprocessing limitations, computational and storage complexity of the event model, and user interaction. The hidden Markov model (HMM) and its variants have been widely used to model both speech and video signals. Computational efficiency of the Baum-Welch and the Viterbi algorithms has been a leading reason for the popularity of the HMM. Since the objective is to detect events in video sequences that are meaningful to humans, one might want to provide space in the design loop for a user who can specify events of interest. This chapter explores this using semantic approaches that not only use features extracted from raw video streams but also incorporate metadata and ontologies of activities. It presents three approaches for applications such as event recognition: anomaly detection, temporal segmentation, and ontology evaluation. The three approaches discussed are statistical methods based on HMMs, formal grammars, and ontologies. The effectiveness of these approaches is illustrated using video sequences captured both indoors and outdoors: the indoor UCF human action dataset, the TSA airport tarmac surveillance dataset, and the bank monitoring dataset.

Original languageEnglish (US)
Title of host publicationUnderstanding Events
Subtitle of host publicationFrom Perception to Action
PublisherOxford University Press
ISBN (Electronic)9780199870462
ISBN (Print)9780195188370
DOIs
StatePublished - May 1 2008
Externally publishedYes

Keywords

  • Baum-Welch
  • Computational efficiency
  • Event modelling systems
  • Event perception
  • Event recognition
  • Hidden markov model
  • Video sequences
  • Viterbi

ASJC Scopus subject areas

  • Psychology(all)

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  • Cite this

    Chellappa, R., Cuntoor, N. P., Joo, S. W., Subrahmanian, V. S., & Turaga, P. (2008). Computational Vision Approaches for Event Modeling. In Understanding Events: From Perception to Action Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195188370.003.0021