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Temporal saliency adaptation in egocentric videos

Panagiotis, Linardos, Mohedano, Eva, Cherto, Monica, Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 and Giró-i-Nieto, Xavier orcid logoORCID: 0000-0002-9935-5332 (2018) Temporal saliency adaptation in egocentric videos. In: The Third International Workshop on Egocentric Perception, Interaction and Computing (EPIC@ECCV18), 9 Sept 2018, Munich, Germany.

Abstract
This work adapts a deep neural model for image saliency prediction to the temporal domain of egocentric video. We compute the saliency map for each video frame, firstly with an off-the-shelf model trained from static images, secondly by adding a a convolutional or conv-LSTM layers trained with a dataset for video saliency prediction. We study each configuration on EgoMon, a new dataset made of seven egocentric videos recorded by three subjects in both free-viewing and task-driven set ups. Our results indicate that the temporal adaptation is beneficial when the viewer is not moving and observing the scene from a narrow field of view. Encouraged by this observation, we compute and publish the saliency maps for the EPIC Kitchens dataset, in which view- ers are cooking.
Metadata
Item Type:Conference or Workshop Item (Poster)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Digital video
Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Copyright Information:© The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI/12/RC/2289
ID Code:22679
Deposited On:19 Sep 2018 13:02 by Eva Mohedano Robles . Last Modified 22 Oct 2018 10:48
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