Abstract
Most statistical background subtraction techniques are based on the analysis of temporal color/intensity distributions. However, learning statistics on a series of time frames can be problematic, especially when no frames absent of moving objects are available or when the available memory isn’t sufficient to store the series of frames needed for learning. In this paper, we propose a framework that allows common statistical motion detection methods to use spatial statistics gathered on one frame instead of a series of frames as is usually the case. This simple and flexible framework is suitable for various applications including the ones with a mobile background such as when a tree is shaken by wind or when the camera jitters. Three statistical background subtraction methods have been adapted to the proposed framework and tested on different synthetic and real image sequences.
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Jodoin, PM., Mignotte, M., Konrad, J. (2006). Background Subtraction Framework Based on Local Spatial Distributions. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_35
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DOI: https://doi.org/10.1007/11867586_35
Publisher Name: Springer, Berlin, Heidelberg
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