Performance characterization for Gaussian mixture model based motion detection algorithms

Pixelwise Gaussian mixture based background modeling algorithm proposed in S. Stauffer and W.E.L. Grimson (1999 and 2000) has been proved to be robust for many motion detection applications. However, the algorithm is not sensitive to fast motion. One possible solution is to introduce local correlations. Starting from this, this paper proposes to use the Gaussian mixture globally for modeling the distribution of the difference image between the new frame and the estimated background. Experimental evaluation validates the algorithm. Motivated by the demands of selecting the more appropriate algorithm for a specific application, qualitative and quantitative performance comparisons of these two approaches are presented. We proposes three metrics. One is to characterize the pixel level accuracy and the other two are to evaluate the errors in the object level. Pros and cons of both algorithms are summarized.