Story-Driven Summarization for Egocentric Video

We present a video summarization approach that discovers the story of an egocentric video. Given a long input video, our method selects a short chain of video sub shots depicting the essential events. Inspired by work in text analysis that links news articles over time, we define a random-walk based...

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Bibliographic Details
Published in2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 2714 - 2721
Main Authors Zheng Lu, Grauman, Kristen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Adaptation models
Cameras
egocentric
Histograms
Image color analysis
Joining processes
Measurement
story
video summarization
Visualization
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Summary:We present a video summarization approach that discovers the story of an egocentric video. Given a long input video, our method selects a short chain of video sub shots depicting the essential events. Inspired by work in text analysis that links news articles over time, we define a random-walk based metric of influence between sub shots that reflects how visual objects contribute to the progression of events. Using this influence metric, we define an objective for the optimal k-subs hot summary. Whereas traditional methods optimize a summary's diversity or representative ness, ours explicitly accounts for how one sub-event "leads to" another-which, critically, captures event connectivity beyond simple object co-occurrence. As a result, our summaries provide a better sense of story. We apply our approach to over 12 hours of daily activity video taken from 23 unique camera wearers, and systematically evaluate its quality compared to multiple baselines with 34 human subjects.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.350