Background Initialization with A New Robust Statistical Approach
Hanzi Wang, David Suter · 2006
Initializing a background model requires robust statistical methods as the task should be robust against random occurrences of foreground objects, as well as against general image noise. The median has been employed for the problem of background initialization. However, the median has only a breakdown point of 50%. In this paper, we propose a new robust method which can tolerate more than 50% of noise and foreground pixels in the background initialization process. We compare our new method with five others and give quantitative evaluations on background initialization. Experiments show that the proposed method achieves very promising results in background initialization.