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Background Modeling by Weightless Neural Networks

  • Massimo De Gregorio
  • Maurizio GiordanoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

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.

Keywords

Background Modeling Random Access Memory General Regression Neural Network Foreground Object Pixel Color 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Istituto di Scienze Applicate e Sistemi Intelligenti – CNRPozzuoliItaly
  2. 2.Istituto di Calcolo e Reti ad Alte Prestazioni – CNRNaplesItaly

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