Egocentric cameras are becoming more popular, introducing increasing volumes of video in which the biases and framing of traditional photography are replaced with those of natural viewing tendencies. This paradigm enables new applications, including novel studies of social interaction and human development. Recent work has focused on identifying the camera wearer's hands as a first step towards more complex analysis. In this paper, we study how to disambiguate and track not only the observer's hands but also those of social partners. We present a probabilistic framework for modeling paired interactions that incorporates the spatial, temporal, and appearance constraints inherent in egocentric video. We test our approach on a dataset of over 30 minutes of video from six pairs of subjects.
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