Total Variation Regularized RPCA for Irregularly Moving Object Detection Under Dynamic Background
- 20 April 2015
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Cybernetics
- Vol. 46 (4), 1014-1027
- https://doi.org/10.1109/tcyb.2015.2419737
Abstract
Moving object detection is one of the most fundamental tasks in computer vision. Many classic and contemporary algorithms work well under the assumption that backgrounds are stationary and movements are continuous, but degrade sharply when they are used in a real detection sreal detection systemystem, mainly due to: 1) the dynamic background (e.g., swaying trees, water ripples and fountains in real scenarios, as well as raindrops and snowflakes in bad weather) and 2) the irregular object movement (like lingering objects). This paper presents a unified framework for addressing the difficulties mentioned above, especially the one caused by irregular object movement. This framework separates dynamic background from moving objects using the spatial continuity of foreground, and detects lingering objects using the temporal continuity of foreground. The proposed framework assumes that the dynamic background is sparser than the moving foreground that has smooth boundary and trajectory. We regard the observed video as being made up of the sum of a low-rank static background, a sparse and smooth foreground, and a sparser dynamic background. To deal with this decomposition, i.e., a constrained minimization problem, the augmented Lagrangian multiplier method is employed with the help of the alternating direction minimizing strategy. Extensive experiments on both simulated and real data demonstrate that our method significantly outperforms the state-of-the-art approaches, especially for the cases with dynamic backgrounds and discontinuous movements.Keywords
Funding Information
- National Natural Science Foundation of China (61422213, 61332012, 61402467)
- 100 Talents Programme of The Chinese Academy of Sciences
- Excellent Young Talent Programme through the Institute Information Engineering, Chinese Academy of Sciences
- Foundation for the Young Scholars by the Tianjin University of Commerce (150113)
- National Training Programs of Innovation and Entrepreneurship for Undergraduates (201410069040)
This publication has 40 references indexed in Scilit:
- Robust principal component analysis?Journal of the ACM, 2011
- A survey on vision-based human action recognitionImage and Vision Computing, 2009
- DYNAMIC BACKGROUND SUBTRACTION BASED ON LOCAL DEPENDENCY HISTOGRAMInternational Journal of Pattern Recognition and Artificial Intelligence, 2009
- Object trackingACM Computing Surveys, 2006
- A survey of advances in vision-based human motion capture and analysisComputer Vision and Image Understanding, 2006
- Statistical Modeling of Complex Backgrounds for Foreground Object DetectionIEEE Transactions on Image Processing, 2004
- An Algorithm for Total Variation Minimization and ApplicationsJournal of Mathematical Imaging and Vision, 2004
- WEATHER, SEASONAL TRENDS AND PROPERTY CRIMES IN MINNEAPOLIS, 1987–1988. A MODERATOR-VARIABLE TIME-SERIES ANALYSIS OF ROUTINE ACTIVITIESJournal of Environmental Psychology, 2000
- Nonlinear total variation based noise removal algorithmsPhysica D: Nonlinear Phenomena, 1992
- Lunar Effects on Mental BehaviorEnvironment and Behavior, 1982