Current Back-Ground Subtraction (BGS) algorithms are pixel-based methods. We propose an Interest-Point(IP)-based BGS algorithm applicable in IP-based Computer Vision application. Based on a block-wiseprocessing strategy, the images are divided into blocks of the same size. IPs inside blocks are dealt withtogether as Events. Throughout the frames, the algorithm stores Events of blocks as well as the numbersof their occurrences (Repetition Index (RI)) in a Binary Tree. The RI is used to classify Events into thebackground and foreground. The background Events appear significantly more than a threshold. The otherswith RI value less than the threshold, are classified as the foreground Events. This event classification isused to label IPs of frames into the foreground and background IPs. Experimental results quantitativelyshow that the proposed algorithm delivers a good subtraction rate in comparison with the other BGS ap-proaches. Moreover, it: creates a map of the background usable for further processing; is robust to changesin illumination; and can keep itself updated to changes in the background.
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