Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/71242
Título: Video indexing using face appearance and shot transition detection
Autores/as: Cazzato, Dario
Leo, Marco
Carcagni, Pierluigi
DIstante, Cosimo
Lorenzo-Navarro, Javier 
Voos, Holger
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Assistive technology
Face analysis
Video indexing
Fecha de publicación: 2019
Editor/a: Institute of Electrical and Electronics Engineers (IEEE) 
Publicación seriada: IEEE International Conference on Computer Vision Workshops 
Conferencia: 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 
Resumen: The possibility to automatically index human faces in videos could lead to a wide range of applications such as automatic video content analysis, data mining, on-demand streaming, etc. Most relevant works in the literature gather full indexing of videos in real scenarios by exploiting additional media features (e.g. audio and text) that are fused with facial appearance information to make the whole frameworks accurate and robust. Anyway, there exist some application contexts where multimedia data are either not available or reliable and for which available solutions are not well suited. This paper tries to explore this challenging research path by introducing a new fully computer vision based video indexing pipeline. The system has been validated and tested in two different typical scenarios where no-multimedia data could be exploited: broadcasted political video documentaries and healthcare therapies sessions about non-verbal skills.
URI: http://hdl.handle.net/10553/71242
ISBN: 978-1-7281-5024-6
ISSN: 2473-9936
DOI: 10.1109/ICCVW.2019.00319
Fuente: Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, p. 2611-2618
Colección:Actas de congresos
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