dc.contributor.author | Rodríguez Moreno, Itsaso | |
dc.contributor.author | Martínez Otzeta, José María | |
dc.contributor.author | Sierra Araujo, Basilio | |
dc.contributor.author | Rodríguez Rodríguez, Igor | |
dc.contributor.author | Jauregi Iztueta, Ekaitz | |
dc.date.accessioned | 2020-02-24T09:12:28Z | |
dc.date.available | 2020-02-24T09:12:28Z | |
dc.date.issued | 2019-07-18 | |
dc.identifier.citation | Sensors 19(14) : (2019) // Article ID 3160 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/41403 | |
dc.description | This article belongs to the Section Physical Sensors. | es_ES |
dc.description.abstract | Video activity recognition, although being an emerging task, has been the subject of important research efforts due to the importance of its everyday applications. Surveillance by video cameras could benefit greatly by advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. The aim of this paper is to survey the state-of-the-art techniques for video activity recognition while at the same time mentioning other techniques used for the same task that the research community has known for several years. For each of the analyzed methods, its contribution over previous works and the proposed approach performance are discussed. | es_ES |
dc.description.sponsorship | This work has been partially supported by the Basque Government, Spain (IT900-16), the Spanish Ministry of Economy and Competitiveness (RTI2018-093337-B-I00, MINECO/FEDER, EU). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/RTI2018-093337-B-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | activity recognition | es_ES |
dc.subject | computer vision | es_ES |
dc.subject | optical flow | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | binet-cauchy kernels | es_ES |
dc.subject | bidirectional lstm | es_ES |
dc.subject | dynamical-systems | es_ES |
dc.subject | neural-networks | es_ES |
dc.subject | optical-flow | es_ES |
dc.subject | motion | es_ES |
dc.subject | classification | es_ES |
dc.subject | histograms | es_ES |
dc.subject | surveillance | es_ES |
dc.subject | categories | es_ES |
dc.title | Video Activity Recognition: State-of-the-Art | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/19/14/3160 | es_ES |
dc.identifier.doi | 10.3390/s19143160 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoes | Lenguajes y sistemas informáticos | es_ES |
dc.departamentoeu | Hizkuntza eta sistema informatikoak | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |