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Egocentric hand detection via region growth

Wearable cameras used to record daily life are attracting researchers' attention, and a large number of ego-related applications have been developed in recent years. Hand detection is one of the key steps for the tasks like gesture recognition, action recognition and understanding hand-based interaction in egocentric videos, since humans are accustomed to interacting with objects using their hands. In this work a novel region growth approach is proposed for egocentric hand detection. The proposed method first identifies seed regions most likely containing the parts of hand based on the distribution of hand-related matches. Then hand regions are gradually located by extending from the seed regions. Finally a whole hand is obtained according to the scores of adjacent superpixels, which are evaluated based on four egocentric cues: contrast, distance, previous hand position and skin appearance. The experimental results on two publicly available datasets demonstrate this work achieves satisfactory performance.