Abstract
Background initialization is the task of computing a background model by processing a set of preliminary frames in a video scene. The initial background estimation serves as bootstrap model for video segmentation of foreground objects, although the background estimation could be refined and updated in steady state operation of video processing systems. In this paper we approach the background modeling problem with a weightless neural network called WiSARD $$^{rp}$$ . The proposed approach is straightforward, since the computation is pixel–based and it exploits a dedicated neural network to model the pixel background by using the same training rule.
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