Detecting eye contact using wearable eye-tracking glasses
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Learning to recognize daily actions using gaze
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Video summarization: techniques and classification
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Active labeling application applied to food-related object recognition
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
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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 of objects and people that have never been seen previously. Our results with 17 hours of egocentric data show the method's promise relative to existing techniques for saliency and summarization.