Exponentially weighted update of histogram for background modeling reducing memory usage

Tsubasa Minematsu, Masaki Igarashi, Atsushi Shimada, Hajime Nagahara, Rin Ichiro Taniguchi

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a background model by using an exponentially weighted updating method. We realize to reduce memory usage for construction of background model. Our background model is represented as a histogram according to pixel values. Our model uses an exponential increasing weight for updating our model. In our model, recently observed pixels have a bigger influence on the background model than older ones. Therefore, our model gradually ignores the effect of old-observed value on a background model without retaining past pixel values. We apply our method to background subtraction for comparing with conventional methods using kernel density estimation. In experiments, we conformed that the detection accuracy of our background model is comparable to that of conventional methods.

Original languageEnglish
Pages (from-to)191-200
Number of pages10
JournalJournal of the Institute of Image Electronics Engineers of Japan
Issue number2
Publication statusPublished - 2016

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering


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