Real-time discriminative background subtraction
Date
2011
Authors
Cheng, L.
Gong, M.
Schuurmans, D.
Caelli, T.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IEEE Transactions on Image Processing, 2011; 20(5):1401-1414
Statement of Responsibility
Conference Name
Abstract
The authors examine the problem of segmenting foreground objects in live video when background scene textures change over time. In particular, we formulate background subtraction as minimizing a penalized instantaneous risk functional - yielding a local online discriminative algorithm that can quickly adapt to temporal changes. We analyze the algorithm's convergence, discuss its robustness to nonstationarity, and provide an efficient nonlinear extension via sparse kernels. To accommodate interactions among neighboring pixels, a global algorithm is then derived that explicitly distinguishes objects versus background using maximum a posteriori inference in a Markov random field (implemented via graph-cuts). By exploiting the parallel nature of the proposed algorithms, we develop an implementation that can run efficiently on the highly parallel graphics processing unit (GPU). Empirical studies on a wide variety of datasets demonstrate that the proposed approach achieves quality that is comparable to state-of-the-art offline methods, while still being suitable for real-time video analysis (≥75 fps on a mid-range GPU).
School/Discipline
Dissertation Note
Provenance
Description
Data source: Figures, https://doi.org/10.1109/TIP.2010.2087764
Access Status
Rights
Copyright 2011 IEEE