Learning action dictionaries from video

14 years 7 months ago
Learning action dictionaries from video
Summarizing the contents of a video containing human activities is an important problem in computer vision and has important applications in automated surveillance systems. Summarizing a video requires one to identify and learn a `vocabulary' of action-phrases corresponding to specific events and actions occurring in the video. We propose a generative model for dynamic scenes containing human activities as a composition of independent action-phrases - each of which is derived from an underlying vocabulary. Given a long video sequence, we propose a completely unsupervised approach to learn the vocabulary. Once the vocabulary is learnt, a video segment can be decomposed into a collection of phrases for summarization. We then describe methods to learn the correlations between activities and sequentiality of events. We also propose a novel method for building invariances to spatial transforms in the summarization scheme.
Pavan K. Turaga, Rama Chellappa
Added 20 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2008
Where ICIP
Authors Pavan K. Turaga, Rama Chellappa
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