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.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Pavan K. Turaga, Rama Chellappa
• Keywords: Human Activities, ICIP 2008, Image Processing, Important Problem, Long Video Sequence, Summarization Scheme, Video Segment