基于张量核范数与3D全变分的背景减除

Background subtraction based on tensor nuclear norm and 3D total variation

针对常用背景减除方法忽略前景时空连续性的问题,以及动态背景对前景提取的干扰问题,基于张量鲁棒主成分分析(TRPCA)提出了一种改进的背景减除模型。该模型利用改进的张量核范数对背景进行约束,加强了背景的低秩性,保留了视频的空间信息;然后用3D全变分(3D-TV)对前景进行正则化约束,考虑了目标在时空上的连续性,有效地抑制了动态背景和目标移动对前景提取造成的干扰。实验结果表明,所提算法能有效地分离视频中的前景和背景,且与高阶鲁棒主成分分析(HoRPCA)、带有新核范数的张量鲁棒主成分分析(TRPCA-TNN)和基于克罗内克基的鲁棒主成分分析(KBR-RPCA)等方法相比,综合评判指标F-measure值均处于最优或次优状态。由此可见,所提算法有效地提高了前景背景分离的准确度,抑制了复杂天气和目标移动对前景提取的干扰。

Concerning the fact that common background subtraction methods ignore the spatio-temporal continuity of foreground and the disturbance of dynamic background to foreground extraction,an improved background subtraction model was proposed based on Tensor Robust Principal Component Analysis(TRPCA).The improved tensor nuclear norm was used to constrain the background,which enhanced the low rank of background and retained the spatial information of videos.Then the regularization constraint was performed to the foreground by 3D Total Variation(3D-TV),so as to consider the spatio-temporal continuity of object and effectively suppress the interference of dynamic background and target movement on the foreground extraction.Experimental results show that the proposed model can effectively separate the foreground and background of videos.Compared with High-order Robust Principal Component Analysis(HoRPCA),Tensor Robust Principal Component Analysis with Tensor Nuclear Norm(TRPCA-TNN)and Kronecker-Basis-Representation based Robust Principal Component Analysis(KBR-RPCA),the proposed algorithm has the F-measure values all optimal or sub-optimal.It can be seen that,the proposed model effectively improves the accuracy of foreground and background separation,and suppresses the interference of complex weather and target movement on foreground extraction.