Background subtraction by non-parametric probabilistic clustering

We present a background subtraction approach aimed at efficiency and robustness to common source of disturbance such as gradual and sudden illumination changes, camera gain and exposure variations, noise. At each new frame, a non-parametric mixture-based probabilistic clustering is performed to segment the image into changed and unchanged pixels with respect to a fixed background. A two-components mixture, a two-dimensional discrete feature space, a non-parametric model for the components likelihood and a proper initial guess are the key ingredients of this novel algorithm that, besides dealing effectively with the discrimination of photometric and semantic changes, exhibits very high computational efficiency. Experiments are presented, proving the achieved state-of-the-art robustness-efficiency trade-off.