Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene

Abstract

Determination of moving foreground objects in dynamic scenes for video surveillance systems is still a problem can not be resolved exactly. In the literature; pixel-based, block-based and texture-based methods have been proposed  to solve this problem. The method we propose will be block-based method which can be applied to real time in dynamic scenes. We have created non-overlapped  blocks with the averages the pixels in the gray level. We used this average value to generate the background model based on a modified original KDE (Kernel Density Estimation) method. To determine the moving foreground objects and  to update background model, we use an adaptive parameter which is determined  according to  the number of changes in the state of this pixel during the last N frames. Performance evaluation of the proposed method is tested by background methods in literature without applying post-processing techniques. Experimental results demonstrate the effectiveness and robustness of our method.

Keywords

• Background modeling,Moving object,Background update

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