Parallel multi-modal background modeling

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 (i.e., without foreground objects) frames at the beginning of the sequence in input. However, this assumption is not always true, especially when dealing with dynamic background or crowded scenes. In this paper, we present the results of a multi-modal background modeling method that is able to generate a reliable initial background model even if no clear frames are available. The proposed algorithm runs in real-time on HD images. Quantitative experiments have been conducted taking into account six different quality metrics on a set of 14 publicly available image sequences. The obtained results demonstrate a high-accuracy in generating the background model in comparison with several other methods.