Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2006.25
Title: A dynamic conditional random field model for foreground and shadow segmentation
Authors: Wang, Y.
Loe, K.-F. 
Wu, J.-K.
Keywords: Conditional random fields
Dynamic models
Foreground segmentation
Shadow detection
Issue Date: 2006
Citation: Wang, Y., Loe, K.-F., Wu, J.-K. (2006). A dynamic conditional random field model for foreground and shadow segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2) : 279-289. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2006.25
Abstract: This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences. © 2006 IEEE.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/39293
ISSN: 01628828
DOI: 10.1109/TPAMI.2006.25
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