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2012
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Discovering important people and objects for egocentric video summarization

9 years 2 months ago
Discovering important people and objects for egocentric video summarization
We present a video summarization approach for egocentric or “wearable” camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer’s day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video—such as the nearness to hands, gaze, and frequency of occurrence—and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance ...
Yong Jae Lee, Joydeep Ghosh, Kristen Grauman
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Yong Jae Lee, Joydeep Ghosh, Kristen Grauman
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