GPU-Based Foreground-Background Segmentation using an Extended Colinearity Criterion

We present a GPU-based foreground-background segmentation that processes image sequences in less than 4ms per frame. Change detection wrt. the background is based on a color similarity test in a small pixel neighbourhood, and is integrated into a Bayesian estimation framework. An iterative MRFbased model is applied, exploiting parallelism on modern graphics hardware. Resulting segmentation exhibits compactness and smoothness in foreground areas as well as for inter-frame temporal contiguity. Further renements extend the colinearity criterion with compensation for dark foreground and background areas and thus improving overall performance.

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