Multi-modal Background Model Initialization

Background subtraction is a widely used technique for detecting moving objects in image sequences. Very often background subtraction approaches assume the availability of one or more clear frames (i.e., without foreground objects) at the beginning of the image sequence in input. This strong assumption is not always correct, especially when dealing with dynamic background. In this paper, we present the results of an on-line and real-time background initialization method, called IMBS, which generates a reliable initial background model even if no clear frames are available. The accuracy of the proposed approach is calculated on a set of seven publicly available benchmark sequences. Experimental results demonstrate that IMBS generates accurate background models with respect to eight different quality metrics.