Publisher : The Korean Institute of Broadcast and Media Engineers
DOI : 10.5909/JBE.2016.21.2.268
Title & Authors
Detecting Foreground Objects Under Sudden Illumination Change Using Double Background Models Saeed, Mahmoudpour; Kim, Manbae;
In video sequences, foreground object detection being composed of a background model and a background subtraction is an important part of diverse computer vision applications. However, object detection might fail in sudden illumination changes. In this letter, an illumination-robust background detection is proposed to address this problem. The method can provide quick adaption to current illumination condition using two background models with different adaption rates. Since the proposed method is a non-parametric approach, experimental results show that the proposed algorithm outperforms several state-of-art non-parametric approaches and provides low computational cost.
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