Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene
M.Fatih Savaş · DergiPark (Istanbul University) · 2017
Determination of moving foreground objects in dynamic scenesfor video surveillance systems is still a problem can not be resolved exactly.In the literature; pixel-based, block-based and texture-based methods have beenproposed to solve this problem. Themethod we propose will be block-based method which can be applied to real timein dynamic scenes. We have created non-overlapped blocks with the averages the pixels in thegray level. We used this average value to generate the background model basedon a modified original KDE (Kernel Density Estimation) method. To determine themoving foreground objects and to updatebackground model, we use an adaptive parameter which is determined according to the number of changes in the state of this pixel during the last Nframes. Performance evaluation of the proposed method is tested by backgroundmethods in literature without applying post-processing techniques. Experimentalresults demonstrate the effectiveness and robustness of our method.