IMPROVED SAUVOLA THRESHOLD FOR BACKGROUND SUBTRACTION ON MOVING OBJECT DETECTION
Image Segmentation is one essential processing on moving object detection. The one of common segmentation methods is thresholding. In this paper, Thresholding method based on adaptive local technique using local mean and standard deviation is known as ‘WAN’ method. WAN has been inspired by the Sauvola’s binarization method and exhibits its robustness and effectiveness when evaluated on low quality document images. The objective of the WAN is to enhance the sauvola method and to get a better binarization result and enhance the accuracy. This research aims to produce output value of WAN algorithm. WAN will be compared to other existing adaptive local method like sauvola and niblack. This research is implemented by using matlab and four videos original from camera. The best result calculation error (MSE,PSNR) of WAN method are (0.0011, 53,6655). Overall, the result of WAN method in this paper is more effective and efficient than other existing method based on MSE and PSNR.