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Technical Program
Paper Detail
| Paper: | MP-P8.1 |
| Session: | Motion Detection and Estimation |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
LIGHT AND FAST STATISTICAL MOTION DETECTION METHOD BASED ON ERGODIC MODEL |
| Authors: |
Pierre-Marc Jodoin; Université de Montréal | | | | | Max Mignotte; Université de Montréal | | | | | Janusz Konrad; Boston University | | |
| Abstract: |
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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