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.
|Title of host publication||Human behavior understanding: Second International Workshop, HBU 2011, Amsterdam, The Netherlands, November 16, 2011|
|Editors||Albert Ali Salah, Bruno Lepri|
|Place of Publication||Berlin, Germany|
|Number of pages||12|
|Publication status||Published - 26 Jul 2011|
|Name||Lecture notes in computer science|
Chen, C-W., Aztiria, A., Ben Allouch, S., & Aghajan, H. (2011). Understanding the influence of social interactions on individual's behavior pattern in a work environment. In A. A. Salah, & B. Lepri (Eds.), Human behavior understanding: Second International Workshop, HBU 2011, Amsterdam, The Netherlands, November 16, 2011 (pp. 146-157). (Lecture notes in computer science; Vol. 7065). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-25446-8_16