ISSN : 1796-2048
Volume : 2    Issue : 5    Date : September 2007

Object Segmentation Using Background Modelling and Cascaded Change Detection
Luís F. Teixeira, Jaime S. Cardoso, and Luís Corte-Real
Page(s): 55-65
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The automatic extraction and analysis of visual information is becoming generalised. The first step
in this processing chain is usually separating or segmenting the captured visual scene in individual
objects. Obtaining a perceptually correct segmentation is however a cumbersome task. Moreover,
typical applications relying on object segmentation, such as visual surveillance, introduce two
additional requirements: (1) it should represent only a small fraction of the total amount of
processing time and (2) realtime overall processing. We propose a technique that tackles these
problems using a cascade of change detection tests, including noise-induced, illumination variation
and structural changes. An objective comparison of common pixelwise modelling methods is first
done. A cost-based partitiondistance between segmentation masks is introduced and used to
evaluate the methods. Both the mixture of Gaussians and the kernel density estimation are used as
a base to detect structural changes in the proposed algorithm. Experimental results show that the
cascade technique consistently outperforms the base methods, without additional post-processing
and without additional processing overheads.

Index Terms
foreground segmentation, object detection, background modelling and subtraction, objective
comparison of segmentations, cost-based partition-distance, cascaded change detection, visual