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Sparse Occlusion Detection with Optical Flow
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DOI：10.1007/s11263-011-0490-7.png)
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摘要
En 中文
We tackle the problem of detecting occluded regions in a video stream. Under assumptions of Lambertian reflection and static illumination, the task can be posed as a variational optimization problem, and its solution approximated using convex minimization. We describe efficient numerical schemes that reach the global optimum of the relaxed cost functional, for any number of independently moving objects, and any number of occlusion layers. We test the proposed algorithm on benchmark datasets, expanded to enable evaluation of occlusion detection performance, in addition to optical flow.
Keyword:
Occlusion detection
Optical flow
Convex optimization
Sparse optimization
Nesterov's algorithm
Split-Bregman method
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