Date of Original Version

6-2009

Type

Conference Proceeding

Published In

Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp.2012-2019, 20-25 June 2009

Abstract or Table of Contents

Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story/plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data.

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Robotics Commons

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