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Background subtraction - separating the modeling and the inference
In particular, it is essential to have a background likelihood, a foreground likelihood, and a prior at each pixel.
Background Modeling Using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space
In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussians at each pixel [7], to kernel density estimates at each pixel [1], and more recently to joint domainrange density estimates that incorporate spatial information [6].
Coherent Motion Segmentation in Moving Camera Videos using Optical Flow Orientations
Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene.
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