Detected motion classification with a double-background and a Neighborhood-based difference ?
This paper describes a new method to detect moving objects in a dynamic scene based on background subtraction. The main goal of the method is to obtain and keep a stable background image to cope with variations on environmental changing conditions. In this way, we use a double background (long-term background and short-term background) to deal with temporal stability and fast changes. In addition, this method computes the temporal changes in the video sequence by a local convolution mask taking into account the information of the pixel neighborhood, being less sensitive to noise. Besides, the method classifies the regions of change in moving and static blobs. The first ones represent real moving objects, and the second are related to illumination changes and noise. Finally, experimental results and a performance measure establishing the confidence of the method are presented.
background subtraction illumination changes change detection background updating