IEEE Trans Image Process - Real-time discriminative background subtraction.

Tópicos

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Resumo

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).

Resumo Limpo

author examin problem segment foreground object live video background scene textur chang time particular formul background subtract minim penal instantan risk functionalyield local onlin discrimin algorithm can quick adapt tempor chang analyz algorithm converg discuss robust nonstationar provid effici nonlinear extens via spars kernel accommod interact among neighbor pixel global algorithm deriv explicit distinguish object versus background use maximum posteriori infer markov random field implement via graphcut exploit parallel natur propos algorithm develop implement can run effici high parallel graphic process unit gpu empir studi wide varieti dataset demonstr propos approach achiev qualiti compar stateoftheart offlin method still suitabl realtim video analysi fps midrang gpu

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