Real-Time Discriminative Background Subtraction

Li Cheng, Minglun Gong, Dale Schuurmans, Terry Caelli
Bioinformatics Institute, A*STAR, Singapore, Singapore
IEEE Transactions on Image Processing, 2010


   title={Real-time discriminative background subtraction},

   author={Cheng, L. and Gong, M. and Schuurmans, D. and Caelli, T.},

   journal={Image Processing, IEEE Transactions on},






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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).
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