Light and Fast Statistical Motion Detection Method Based on Ergodic Model

In this paper, we propose a light and fast pixel-based statistical motion detection method based on a background subtraction procedure. The statistical representation of the background relies on its spatial color distributions herein modeled by a mixture of Gaussians. The Gaussian parameters are obtained after segmenting one reference frame with an unsupervised Bayesian approach whose parameter estimation step is ensured by the K-means and the iterated conditional estimation (ICE) algorithms. Since the motion detection function only depends on a global mixture of M Gaussians, only a few bits per pixel need to be stored in memory. Our method achieves real-time performances, especially when look up tables are used to store pre-calculated data. Results have been obtained on synthetic and real video sequences and compared with other statistical methods.

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