Current Background Subtraction (BGS) algorithms are mostly pixel-based methods. We propose an Interest-Point(IP)-based BGS algorithm applicable in IP-based Computer Vision applications. Based on a block-wise processing strategy, the frames are divided into blocks of the same size. IPs inside each block are together Events. Throughout the frame sequence, the algorithm stores the Events in each block as well as the numbers of their occurrences (Repetition Index (RI)) in a Binary Tree. The RI is used to classify Events as either background or foreground. The background Events appear significantly more often than foreground Events. Events with an RI greater than a certain threshold are classified as background, the rest as foreground. This Event classification is used to label IPs of frames into the foreground and background IPs. Experimental results quantitatively show that the proposed algorithm delivers a good subtraction rate in comparison with other BGS approaches. Moreover, it creates a map of the background usable for further processing, it is robust to changes in illumination and can keep itself updated to changes in the background.
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