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Détection temporelle de saillance dynamique dans des vidéos par apprentissage profond

Abstract : We address the problem of motion saliency in videos. More precisely, we aim to determine at each time step if an image can be classified as salient or not according to its motion content. An image will be detected as salient if it contains objects whose motion departs from its spatio-temporal context. The proposed approach handles situations with a mobile camera and involves a deep learning stage. Several variants are proposed and compared. Temporal saliency detection is relevant for applications that require to trigger alerts or to monitor dynamic behaviours from videos. Experiments on real videos demonstrate that the proposed methods can provide accurate classification.
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Contributor : Léo Maczyta Connect in order to contact the contributor
Submitted on : Monday, November 19, 2018 - 10:51:21 AM
Last modification on : Wednesday, November 3, 2021 - 8:15:02 AM
Long-term archiving on: : Wednesday, February 20, 2019 - 1:18:12 PM


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  • HAL Id : hal-01926351, version 1


Léo Maczyta, Patrick Bouthemy, Olivier Le Meur. Détection temporelle de saillance dynamique dans des vidéos par apprentissage profond. RFIAP 2018 - Reconnaissance des Formes, Image, Apprentissage et Perception, Jun 2018, Marne-la-Vallée, France. pp.1-8. ⟨hal-01926351⟩



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