An Updating Method of Self-adaptive Background for Moving Objects Detection in Video

摘要

Background subtraction is a general and simple method in real-time detection of moving object. However, it requires the accurate current background image, and so far, no reasonable approach has been designed and implemented for automatic background updating along with the illumination variance, which limits its applications. To overcome the above problem, a new self-adaptive background approximating and updating algorithm based on static background and kalman self-adaptive background(KAB) is presented in this paper.Moreover, the two renewal rates in KAB are obtained by the cumulants of background subtraction in object region and background region. Experimental results demonstrate that the proposed new background updating method can update the background exactly and quickly along with the variance of illumination, the renewal rates can vary with the video automatically and they bring certain noise immunity to the new dynamic background.