TY - JOUR
T1 - Foreground-Background Separation from Video Clips via Motion-Assisted Matrix Restoration
AU - Ye, Xinchen
AU - Yang, Jingyu
AU - Sun, Xin
AU - Li, Kun
AU - Hou, Chunping
AU - Wang, Yao
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61372084 and Grant 61302059 and in part by the Tianjin Research Program of Application Foundation and Advanced Technology under Grant 12JCYBJC10300 and Grant 13JCQNJC03900. This paper was recommended by Associate Editor J. Lu. The authors would like to thank the reviewers, whose comments improved this paper.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/11
Y1 - 2015/11
N2 - Separation of video clips into foreground and background components is a useful and important technique, making recognition, classification, and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground-background separation in video clips. In the proposed MAMR model, the backgrounds across frames are modeled by a low-rank matrix, while the foreground objects are modeled by a sparse matrix. To facilitate efficient foreground-background separation, a dense motion field is estimated for each frame, and mapped into a weighting matrix which indicates the likelihood that each pixel belongs to the background. Anchor frames are selected in the dense motion estimation to overcome the difficulty of detecting slowly moving objects and camouflages. In addition, we extend our model to a robust MAMR model against noise for practical applications. Evaluations on challenging datasets demonstrate that our method outperforms many other state-of-the-art methods, and is versatile for a wide range of surveillance videos.
AB - Separation of video clips into foreground and background components is a useful and important technique, making recognition, classification, and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground-background separation in video clips. In the proposed MAMR model, the backgrounds across frames are modeled by a low-rank matrix, while the foreground objects are modeled by a sparse matrix. To facilitate efficient foreground-background separation, a dense motion field is estimated for each frame, and mapped into a weighting matrix which indicates the likelihood that each pixel belongs to the background. Anchor frames are selected in the dense motion estimation to overcome the difficulty of detecting slowly moving objects and camouflages. In addition, we extend our model to a robust MAMR model against noise for practical applications. Evaluations on challenging datasets demonstrate that our method outperforms many other state-of-the-art methods, and is versatile for a wide range of surveillance videos.
KW - Background segmentation/subtraction
KW - matrix restoration
KW - motion detection
KW - optical flow
KW - video surveillance
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U2 - 10.1109/TCSVT.2015.2392491
DO - 10.1109/TCSVT.2015.2392491
M3 - Article
AN - SCOPUS:84960474011
SN - 1051-8215
VL - 25
SP - 1721
EP - 1734
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 11
M1 - 7014298
ER -