Moving-object detection is important in many computer vision tasks. Background modeling is a traditional and usual method for moving-object detection. However

most of these methods are pixel-based

which only make overly simple considerations on background and encounter difficulty in handling real videos. Recently

robust principal component analysis (RPCA)

which is based on low rank and sparse decomposition

has been studied in the field of moving-object detection by a growing number of researchers. To enable more researchers to have a comprehensive understanding of RPCA and to employ RPCA in moving-object detection

this survey conducts a thorough review of moving-object detection algorithms based on RPCA. In recent years

RPCA has received substantial attention from researchers in computer vision because of its excellent advantages of capturing slight variations in background appearance via low-rank matrix. Until today

many improved algorithms and applications based on RPCA have emerged in the computer vision field. In this paper

recent studies in moving-object detection based on RPCA are reviewed in detail. We classify those RPCA-based-moving detection methods into five categories

which are error mitigation

Bayesian theory

temporal and spatial information

multi-feature

and multi-factor coupling. In addition

this context summarizes and analyses studies on RPCA methods and their applications to moving-object detection locally and internationally. We employ the change detection dataset to compare the performances of the methods in different categories and the original RPCA. We use metrics such as recall

precision

F-measure

and time consumed for objective evaluation. Also

we illustrate foreground segmentation results achieved by those methods for subjective evaluation. Experimental results indicate that these improvements have solved certain problems in the original RPCA and have achieved more excellent performance compared with the original RPCA. RPCA is a popular research topic in computer vision field today. However

RPCA has certain limitations

which should be studied further. Involving video prior knowledge of foreground objects in RPCA is an emerging trend in the future.