Abstract
This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to save computation time and be robust to noise effect, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest. Next, we consider the motion of the foreground objects, and hence utilize the temporal coherence property on the regions detected. Thus, foreground segmentation problem is formulated as follows. Given two consecutive image frames and the segmentation result obtained priorly, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner. To represent the conditional joint probability density function in a compact form, a Bayesian network is adopted, which is derived to model the interdependency of these two elements. Experimental results for several video sequences are provided to demonstrate the effectiveness of our proposed approach.
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Huang, SS., Fu, LC., Hsiao, PY. (2007). A Bayesian Network for Foreground Segmentation in Region Level. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_13
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DOI: https://doi.org/10.1007/978-3-540-76390-1_13
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