The understanding of mutual people interaction is a key component for recognizing people social behavior, but it strongly relies on a personal point of view resulting difficult to be a-priori modeled. We propose the adoption of the unique head mounted cameras first person perspective (ego-vision) to promptly detect people interaction in different social contexts. The proposal relies on a complete and reliable system that extracts people׳s head pose combining landmarks and shape descriptors in a temporal smoothed HMM framework. Finally, interactions are detected through supervised clustering on mutual head orientation and people distances exploiting a structural learning framework that specifically adjusts the clustering measure according to a peculiar scenario. Our solution provides the flexibility to capture the interactions disregarding the number of individuals involved and their level of acquaintance in context with a variable degree of social involvement. The proposed system shows competitive performances on both publicly available ego-vision datasets and ad hoc benchmarks built with real life situations.

Understanding social relationships in egocentric vision / Alletto, Stefano; Serra, Giuseppe; Calderara, Simone; Cucchiara, Rita. - In: PATTERN RECOGNITION. - ISSN 0031-3203. - ELETTRONICO. - 48:12(2015), pp. 4082-4096. [10.1016/j.patcog.2015.06.006]

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