TY  - JOUR
ID  - SisLab3297
UR  - https://www.mdpi.com/2313-433X/4/7/90
IS  - 7
A1  - Prativadibhayankaram, Srivatsa
A1  - Huynh, Van Luong
A1  - Le, Thanh Ha
A1  - Kaup, André
Y1  - 2018///
N2  - In the context of video background?foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method operates on a small set of measurements taken per frame, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-?1 minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background?foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.
JF  - Journal of Imaging
VL  - 4
TI  - Compressive Online Video Background?Foreground Separation Using Multiple Prior Information and Optical Flow
AV  - none
ER  -