Show simple item record

AuthorJaved, Sajid
AuthorMahmood, Arif
AuthorAl-Maadeed, Somaya
AuthorBouwmans, Thierry
AuthorJung, Soon Ki
Available date2020-05-15T00:15:02Z
Publication Date2019
Publication NameIEEE Transactions on Image Processing
ISSN10577149
URIhttp://dx.doi.org/10.1109/TIP.2018.2874289
URIhttp://hdl.handle.net/10576/14895
AbstractMoving object detection is a fundamental step in various computer vision applications. Robust principal component analysis (RPCA)-based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in illumination. It is because of an underlying assumption that the elements in the sparse component are mutually independent, and thus the spatiotemporal structure of the moving objects is lost. To address this issue, we propose a spatiotemporal structured sparse RPCA algorithm for moving objects detection, where we impose spatial and temporal regularization on the sparse component in the form of graph Laplacians. Each Laplacian corresponds to a multi-feature graph constructed over superpixels in the input matrix. We enforce the sparse component to act as eigenvectors of the spatial and temporal graph Laplacians while minimizing the RPCA objective function. These constraints incorporate a spatiotemporal subspace structure within the sparse component. Thus, we obtain a novel objective function for separating moving objects in the presence of complex backgrounds. The proposed objective function is solved using a linearized alternating direction method of multipliers based batch optimization. Moreover, we also propose an online optimization algorithm for real-time applications. We evaluated both the batch and online solutions using six publicly available data sets that included most of the aforementioned challenges. Our experiments demonstrated the superior performance of the proposed algorithms compared with the current state-of-the-art methods.
SponsorManuscript received January 5, 2018; revised July 4, 2018 and September 15, 2018; accepted September 27, 2018. Date of publication October 8, 2018; date of current version October 23, 2018. This work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant NPRP 7-1711-1-312. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Raja Bala. (Corresponding author: Soon Ki Jung.) S. Javed is with the Department of Computer Science, The University of Warwick, Coventry CV4 7AL, U.K. (e-mail: s.javed.1@warwick.ac.uk).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBackground subtraction
Subjectforeground detection
Subjectmoving objects detection
SubjectRPCA
Subjectspatiotemporal regularization
TitleMoving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA
TypeArticle
Pagination1007-1022
Issue Number2
Volume Number28


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record