Predicting Important 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 wearers 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 videosuch as the nearness to hands, gaze, and frequency of occurrenceand 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. We adjust the compactness of the final summary given either an importance selection criterion or a length budget; for the latter, we design an efficient dynamic programming solution that accounts for importance, visual uniqueness, and temporal displacement. 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 of objects and people that have never been seen previously. Our results on two egocentric video datasets show the methods promise relative to existing techniques for saliency and summarization.
video summarization egocentric vision category discovery saliency detection