Bano, Sophia;
Zhang, Jianguo;
McKenna, Stephen J.;
(2018)
Finding Time Together: Detection and Classification of Focused Interaction in Egocentric Video.
In:
Proceedings of 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
(pp. pp. 2322-2330).
IEEE: Venice, Italy.
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Abstract
Focused interaction occurs when co-present individuals, having mutual focus of attention, interact by establishing face-to-face engagement and direct conversation. Face-to-face engagement is often not maintained throughout the entirety of a focused interaction. In this paper, we present an online method for automatic classification of unconstrained egocentric (first-person perspective) videos into segments having no focused interaction, focused interaction when the camera wearer is stationary and focused interaction when the camera wearer is moving. We extract features from both audio and video data streams and perform temporal segmentation by using support vector machines with linear and non-linear kernels. We provide empirical evidence that fusion of visual face track scores, camera motion profile and audio voice activity scores is an effective combination for focused interaction classification.
| Type: | Proceedings paper |
|---|---|
| Title: | Finding Time Together: Detection and Classification of Focused Interaction in Egocentric Video |
| Event: | 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) |
| ISBN-13: | 9781538610343 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICCVW.2017.274 |
| Publisher version: | https://doi.org/10.1109/ICCVW.2017.274 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10066401 |
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