Adaptive Update Background Model for Detecting Moving Objects

An adaptive background updating model is proposed to tackle possible background changes in moving object detection.The model uses feature points to classify the pixels in the scene,then compute their updating rates according to their classification information.Finally,background is updated adaptively with varying update rate.The experimental results show that the proposed model can effectively cope with the inaccurate updating problem in the Gaussian mixture model due to the fixed update rate.