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Proceedings Paper

Towards social interaction detection in egocentric photo-streams
Author(s): Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
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Paper Abstract

Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose.

Paper Details

Date Published: 8 December 2015
PDF: 5 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987514 (8 December 2015); doi: 10.1117/12.2228606
Show Author Affiliations
Maedeh Aghaei, Univ. of Barcelona (Spain)
Mariella Dimiccoli, Univ. of Barcelona (Spain)
Petia Radeva, Univ. of Barcelona (Spain)

Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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