Translational and rotational jitter invariant incremental principal component pursuit for video background modeling

Paul Rodriguez, Brendt Wohlberg

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

33 Citas (Scopus)

Resumen

While Principal Component Pursuit (PCP) is currently considered to be the state of the art method for video background modeling, it suffers from a number of limitations, including a high computational cost, a batch operating mode, and sensitivity to camera jitter. In this paper we propose a novel fully incremental PCP algorithm for video background modeling that is robust to translational and rotational jitter. It processes one frame at a time, obtaining similar results to standard batch PCP algorithms, while being able to deal with translational and rotational jitter. It also has extremely low memory footprint, and a computational complexity that allows almost real-time processing.

Idioma originalInglés
Título de la publicación alojada2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
EditorialIEEE Computer Society
Páginas537-541
Número de páginas5
ISBN (versión digital)9781479983391
DOI
EstadoPublicada - 9 dic. 2015
EventoIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canadá
Duración: 27 set. 201530 set. 2015

Serie de la publicación

NombreProceedings - International Conference on Image Processing, ICIP
Volumen2015-December
ISSN (versión impresa)1522-4880

Conferencia

ConferenciaIEEE International Conference on Image Processing, ICIP 2015
País/TerritorioCanadá
CiudadQuebec City
Período27/09/1530/09/15

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