In the video surveillance literature, background (BG) subtraction is an important and fundamental issue. In this context, a consistent group of methods operates at region level, evaluating in fixed zones of interest pixel values' statistics, so that a per-pixel foreground (FG) labeling can be performed. In this paper, we propose a novel hybrid, pixel/region, approach for background subtraction. The method, named Spatial-Time Adaptive Per Pixel Mixture Of Gaussian (S-TAPPMOG), evaluates pixel statistics considering zones of interest that change continuously over time, adopting a sampling mechanism. In this way, numerous classical BG issues can be efficiently faced: actually, it is possible to model the background information more accurately in the chromatic uniform regions exhibiting stable behavior, thus minimizing foreground camouflages. At the same time, it is possible to model successfully regions of similar color but corrupted by heavy noise, in order to minimize false FG detections. Such approach, outperforming state of the art methods, is able to run in quasi-real time and it can be used at a basis for more structured background subtraction algorithms.
A Spatial Sampling Mechanism for Effective Background Subtraction
CRISTANI, Marco;MURINO, Vittorio
2006-01-01
Abstract
In the video surveillance literature, background (BG) subtraction is an important and fundamental issue. In this context, a consistent group of methods operates at region level, evaluating in fixed zones of interest pixel values' statistics, so that a per-pixel foreground (FG) labeling can be performed. In this paper, we propose a novel hybrid, pixel/region, approach for background subtraction. The method, named Spatial-Time Adaptive Per Pixel Mixture Of Gaussian (S-TAPPMOG), evaluates pixel statistics considering zones of interest that change continuously over time, adopting a sampling mechanism. In this way, numerous classical BG issues can be efficiently faced: actually, it is possible to model the background information more accurately in the chromatic uniform regions exhibiting stable behavior, thus minimizing foreground camouflages. At the same time, it is possible to model successfully regions of similar color but corrupted by heavy noise, in order to minimize false FG detections. Such approach, outperforming state of the art methods, is able to run in quasi-real time and it can be used at a basis for more structured background subtraction algorithms.
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