In this paper we evaluate our method for Background Modeling Through Dictionary Learning (BMTDL) and sparse coding on the recently proposed Scene Background Initialization (SBI) dataset. The BMTDL, originally proposed in [1] for the specific purpose of detecting the foreground of a scene, leverages on the availability of long time observations, where we can treat foreground objects as noise. The SBI dataset refers to more general scene modeling problems – as for video segmentation, compression or editing – where video sequences may be generally short, and often include foreground objects occupying a large portion on the image for the majority of the sequence. The experimental analysis we report is very promising and show how the BMTDL may be also appropriate for these different and challenging conditions.

BMTDL for scene modeling on the SBI dataset

NOCETI, NICOLETTA;STAGLIANO', ALESSANDRA;VERRI, ALESSANDRO;ODONE, FRANCESCA
2015

Abstract

In this paper we evaluate our method for Background Modeling Through Dictionary Learning (BMTDL) and sparse coding on the recently proposed Scene Background Initialization (SBI) dataset. The BMTDL, originally proposed in [1] for the specific purpose of detecting the foreground of a scene, leverages on the availability of long time observations, where we can treat foreground objects as noise. The SBI dataset refers to more general scene modeling problems – as for video segmentation, compression or editing – where video sequences may be generally short, and often include foreground objects occupying a large portion on the image for the majority of the sequence. The experimental analysis we report is very promising and show how the BMTDL may be also appropriate for these different and challenging conditions.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/855262
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