Abstract
In this work, we study social interactions in a work environment and investigate how the presence of other people changes personal behavior patterns. We design the visual processing algorithms to track multiple people in the environment and detect dyadic interactions using a discriminative classifier. The locations of the users are associated with semantic tasks based on the functions of the areas. Our learning method then deduces patterns from the trajectories of people and their interactions. We propose an algorithm to compare the patterns of a user in the presence and absence of social interactions. We evaluate our method on a video dataset collected in a real office. By detecting interactions, we gain insights in not only how often people interact, but also in how these interactions affect the usual routines of the users.
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Chen, CW., Aztiria, A., Ben Allouch, S., Aghajan, H. (2011). Understanding the Influence of Social Interactions on Individual’s Behavior Pattern in a Work Environment. In: Salah, A.A., Lepri, B. (eds) Human Behavior Understanding. HBU 2011. Lecture Notes in Computer Science, vol 7065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25446-8_16
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DOI: https://doi.org/10.1007/978-3-642-25446-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25445-1
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