7 March 2013 Exploiting visual search theory to infer social interactions
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Abstract
In this paper we propose a new method to infer human social interactions using typical techniques adopted in literature for visual search and information retrieval. The main piece of information we use to discriminate among different types of interactions is provided by proxemics cues acquired by a tracker, and used to distinguish between intentional and casual interactions. The proxemics information has been acquired through the analysis of two different metrics: on the one hand we observe the current distance between subjects, and on the other hand we measure the O-space synergy between subjects. The obtained values are taken at every time step over a temporal sliding window, and processed in the Discrete Fourier Transform (DFT) domain. The features are eventually merged into an unique array, and clustered using the K-means algorithm. The clusters are reorganized using a second larger temporal window into a Bag Of Words framework, so as to build the feature vector that will feed the SVM classifier.
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Paolo Rota, Duc-Tien Dang-Nguyen, Nicola Conci, Nicu Sebe, "Exploiting visual search theory to infer social interactions", Proc. SPIE 8667, Multimedia Content and Mobile Devices, 86670C (7 March 2013); doi: 10.1117/12.2005307; https://doi.org/10.1117/12.2005307
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