An incremental principal component pursuit algorithm via projections onto the ℓ1 ball

Paul Rodriguez, Brendt Wohlberg

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

9 Citas (Scopus)

Resumen

Video background modeling, used to detect moving objects in digital videos, is a ubiquitous pre-processing step in computer vision applications. Principal Component Pursuit (PCP) PCP is among the leading methods for this problem. In this paper we proposed a new convex formulation for PCP, substituting the standard ℓ1 regularization with a projection onto the ℓ1-ball. This formulation offers an advantage over the known incremental PCP methods in practical parameter selection and ghosting suppression, while retaining the ability to be implemented in a fully incremental fashion, keeping all the desired properties related to such PCP methods (low memory footprint, adaptation to changes in the background, computational complexity that allows online processing).

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781509063628
DOI
EstadoPublicada - 20 oct. 2017
Evento24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 - Cusco, Perú
Duración: 15 ago. 201718 ago. 2017

Serie de la publicación

NombreProceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Conferencia

Conferencia24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
País/TerritorioPerú
CiudadCusco
Período15/08/1718/08/17

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