Mostra el registre d'ítem simple
Action recognition based on efficient deep feature learning in the spatio-temporal domain
dc.contributor.author | Husain, Syed Farzad |
dc.contributor.author | Dellen, Babette |
dc.contributor.author | Torras, Carme |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.date.accessioned | 2017-04-21T13:12:57Z |
dc.date.available | 2017-04-21T13:12:57Z |
dc.date.issued | 2016 |
dc.identifier.citation | Husain, S., Dellen, B., Torras, C. Action recognition based on efficient deep feature learning in the spatio-temporal domain. "IEEE robotics and automation letters", 2016, vol. 1, núm. 2, p. 984-991. |
dc.identifier.issn | 2377-3766 |
dc.identifier.uri | http://hdl.handle.net/2117/103626 |
dc.description | © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
dc.description.abstract | Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
dc.subject.other | Computer vision for automation |
dc.subject.other | recognition |
dc.subject.other | visual learning |
dc.subject.other | artificial intelligence |
dc.subject.other | computer vision |
dc.subject.other | pattern classification |
dc.title | Action recognition based on efficient deep feature learning in the spatio-temporal domain |
dc.type | Article |
dc.contributor.group | Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI |
dc.identifier.doi | 10.1109/LRA.2016.2529686 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Pattern recognition::Computer vision |
dc.subject.inspec | Classificació INSPEC::Pattern recognition |
dc.relation.publisherversion | http://ieeexplore.ieee.org/document/7406684/ |
dc.rights.access | Open Access |
local.identifier.drac | 19160450 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Husain, S.; Dellen, B.; Torras, C. |
local.citation.publicationName | IEEE robotics and automation letters |
local.citation.volume | 1 |
local.citation.number | 2 |
local.citation.startingPage | 984 |
local.citation.endingPage | 991 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [163]
-
Articles de revista [376]