Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Gustavo Chau, Paul Rodriguez

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

17 Citas (Scopus)

Resumen

Video background modeling is an important preprocessing stage for various applications and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras. We present a novel, fully incremental PCP algorithm, named incPCP-PTI, that is able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low rank plus additive incremental matrix method capable of handling these scenarios. Results on synthetic videos and CDNET2014 videos show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1844-1852
Número de páginas9
ISBN (versión digital)9781538610343
DOI
EstadoPublicada - 1 jul. 2017
Evento16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italia
Duración: 22 oct. 201729 oct. 2017

Serie de la publicación

NombreProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Volumen2018-January

Conferencia

Conferencia16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
País/TerritorioItalia
CiudadVenice
Período22/10/1729/10/17

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