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Learning to Recognize Complex Actions Using Conditional Random Fields

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Advances in Visual Computing (ISVC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4842))

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Abstract

Surveillance systems that operate continuously generate large volumes of data. One such system is described here, continuously tracking and storing observations taken from multiple stereo systems. Automated event recognition is one way of annotating track databases for faster search and retrieval. Recognition of complex events in such data sets often requires context for successful disambiguation of apparently similar activities. Conditional random fields permit straightforward incorporation of temporal context into the event recognition task. This paper describes experiments in activity learning, using conditional random fields to learn and recognize composite events that are captured by the observation stream.

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Authors and Affiliations

  1. SRI International, 333 Ravenswood Avenue, Menlo Park, CA,  

    Christopher I. Connolly

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  1. Christopher I. Connolly
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Editor information

George Bebis Richard Boyle Bahram Parvin Darko Koracin Nikos Paragios Syeda-Mahmood Tanveer Tao Ju Zicheng Liu Sabine Coquillart Carolina Cruz-Neira Torsten Müller Tom Malzbender

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© 2007 Springer-Verlag Berlin Heidelberg

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Connolly, C.I. (2007). Learning to Recognize Complex Actions Using Conditional Random Fields. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_33

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  • DOI: https://doi.org/10.1007/978-3-540-76856-2_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76855-5

  • Online ISBN: 978-3-540-76856-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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