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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Detecting Snap Points in Egocentric Video with a Web Photo Prior
verfasst von : Bo Xiong, Kristen Grauman
Erschienen in: Computer Vision – ECCV 2014
Verlag: Springer International Publishing
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Wearable cameras capture a first-person view of the world, and offer a hands-free way to record daily experiences or special events. Yet, not every frame is worthy of being captured and stored. We propose to automatically predict
“snap points”
in unedited egocentric video—that is, those frames that look like they could have been intentionally taken photos. We develop a generative model for snap points that relies on a Web photo prior together with domain-adapted features. Critically, our approach avoids strong assumptions about the particular
content
of snap points, focusing instead on their
composition
. Using 17 hours of egocentric video from both human and mobile robot camera wearers, we show that the approach accurately isolates those frames that human judges would believe to be intentionally snapped photos. In addition, we demonstrate the utility of snap point detection for improving object detection and keyframe selection in egocentric video.