Discovering social interactions in real work environments

The goal of this work is to detect pairwise primitive interactions in groups for social interaction analysis in real environments. We propose a system that extracts locations and head poses of people from videos captured in an unconstrained environment, namely a research lab. Our system is designed to work with realistic data capturing natural human interactions. An efficient tracking method based on Chamfer matching finds the head and shoulder silhouettes of people in real-time, and a head orientation classifier estimates their head poses. The location, relative distance and head orientation of people capture the use of space by individuals and their interactive behavioral patterns which are inferred with a probabilistic model. We present quantitative evaluation and experimental results of our system, demonstrating the effectiveness of our proposed approach on challenging real-world data.