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Authors: Yasser Abdelaziz Dahou Djilali 1 ; 2 ; Mohamed Sayah 3 ; Kevin McGuinness 1 and Noel E. O’Connor 1

Affiliations: 1 Insight Center for Data Analytics, Dublin City University, Dublin 9, Ireland ; 2 Institut National des Télécommunications et des TIC, Oran, Algeria ; 3 Université Oran1, FSEA, Oran, Algeria

Keyword(s): Visual Attention, Video Saliency, Deep Learning, 3D CNN.

Abstract: In this paper, we propose a novel 3D CNN architecture that enables us to train an effective video saliency prediction model. The model is designed to capture important motion information using multiple adjacent frames. Our model performs a cubic convolution on a set of consecutive frames to extract spatio-temporal features. This enables us to predict the saliency map for any given frame using past frames. We comprehensively investigate the performance of our model with respect to state-of-the-art video saliency models. Experimental results on three large-scale datasets, DHF1K, UCF-SPORTS and DAVIS, demonstrate the competitiveness of our approach.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Djilali, Y.; Sayah, M.; McGuinness, K. and O’Connor, N. (2020). 3DSAL: An Efficient 3D-CNN Architecture for Video Saliency Prediction. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 27-36. DOI: 10.5220/0008875600270036

@conference{visapp20,
author={Yasser Abdelaziz Dahou Djilali. and Mohamed Sayah. and Kevin McGuinness. and Noel E. O’Connor.},
title={3DSAL: An Efficient 3D-CNN Architecture for Video Saliency Prediction},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={27-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008875600270036},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - 3DSAL: An Efficient 3D-CNN Architecture for Video Saliency Prediction
SN - 978-989-758-402-2
IS - 2184-4321
AU - Djilali, Y.
AU - Sayah, M.
AU - McGuinness, K.
AU - O’Connor, N.
PY - 2020
SP - 27
EP - 36
DO - 10.5220/0008875600270036
PB - SciTePress

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