Background modeling of video frame sequences is a prerequisite for computer
vision applications. Robust principal component analysis(RPCA), which aims
to recover low rank matrix in applications of data mining and machine
learning, has shown improved background modeling performance. Unfortunately,
The traditional RPCA method considers the batch recovery of low rank matrix
of all samples, which leads to higher storage cost. This paper proposes a
novel online motion-aware RPCA algorithm, named OM-RPCAT, which adopt
truncated nuclear norm regularization as an approximation method for of low
rank constraint. And then, Two methods are employed to obtain the motion
estimation matrix, the optical flow and the frame selection, which are
merged into the data items to separate the foreground and background.
Finally, an efficient alternating optimization algorithm is designed in an
online manner. Experimental evaluations of challenging sequences demonstrate
promising results over state-of-the-art methods in online application.
A Novel Robust Principal Component Analysis Algorithm of Nonconvex Rank Approximation
