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Of increasing interest to the computer vision community is to recognize egocentric actions. Conceptually, an egocentric action is largely identifiable by the states of hands and objects. For example, “drinking soda” is essentially composed of two sequential states where one first “takes up the soda can”, then “drinks from the soda can”. While existing algorithms commonly use manually defined states...
In this paper, we propose a novel kernel function for recognizing objects in RGB-D egocentric videos. In order to effectively exploit the varied object appearance in a video, we take a set-based recognition approach and represent the target object using the set of frames contained in the video. Our kernel function measures the similarity of two sets by the minimum distance between the sparse affine...
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