eprintid: 3297
rev_number: 7
eprint_status: archive
userid: 299
dir: disk0/00/00/32/97
datestamp: 2018-12-17 03:01:33
lastmod: 2018-12-17 03:01:33
status_changed: 2018-12-17 03:01:33
type: article
metadata_visibility: show
creators_name: Prativadibhayankaram, Srivatsa
creators_name: Huynh, Van Luong
creators_name: Le, Thanh Ha
creators_name: Kaup, André
creators_id: ltha@vnu.edu.vn
title: Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow
ispublished: pub
subjects: IT
subjects: Scopus
divisions: fac_fit
abstract: 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-ℓ1 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.
date: 2018
official_url: https://www.mdpi.com/2313-433X/4/7/90
full_text_status: none
publication: Journal of Imaging
volume: 4
number: 7
refereed: TRUE
citation:   Prativadibhayankaram, Srivatsa and Huynh, Van Luong and Le, Thanh Ha and Kaup, André  (2018) Compressive Online Video Background–Foreground Separation Using Multiple Prior Information and Optical Flow.  Journal of Imaging, 4  (7).