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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

DAPs: Deep Action Proposals for Action Understanding

verfasst von : Victor Escorcia, Fabian Caba Heilbron, Juan Carlos Niebles, Bernard Ghanem

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.

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Vorheriges Kapitel Multi-view Inverse Rendering Under Arbitrary Illumination and Albedo
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Metadaten
Titel
DAPs: Deep Action Proposals for Action Understanding
verfasst von
Victor Escorcia
Fabian Caba Heilbron
Juan Carlos Niebles
Bernard Ghanem
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46486-2

Electronic ISBN: 978-3-319-46487-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46487-9_47