Comparative study of background subtraction algorithms

Abstract : In this paper, we present a comparative study of several state of the art background subtraction methods. Approaches ranging from simple background subtraction with global thresholding to more sophisticated statistical methods have been implemented and tested on different videos with ground truth. The goal of this study is to provide a solid analytic ground to underscore the strengths and weaknesses of the most widely implemented motion detection methods. The methods are compared based on their robustness to different types of video, their memory requirement, and the computational effort they require. The impact of a Markovian prior as well as some post-processing operators are also evaluated. Most of the videos used in this study come from state-of-the-art benchmark databases and represent different challenges such as poor signal-to-noise ratio, multimodal background motion and camera jitter. Overall, this study not only helps better understand to which type of videos each method suits best but also estimate how better sophisticated methods are, compared to basic background subtraction methods.
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Article dans une revue
Journal of Electronic Imaging, SPIE and IS&T, 2010, 19, 〈10.1117/1.3456695〉
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Yannick Benezeth, Pierre-Marc Jodoin, Bruno Emile, Hélène Laurent, Christophe Rosenberger. Comparative study of background subtraction algorithms. Journal of Electronic Imaging, SPIE and IS&T, 2010, 19, 〈10.1117/1.3456695〉. 〈inria-00545478〉



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