An improved background segmentation method for ghost removals

Video surveillance has become common for the maintenance of security in a wide variety of applications. However, the increasingly large amounts of data produced from multiple video camera feeds is making it increasingly difficult for human operators to monitor the imagery for activities likely to give rise to threats. This has led to the development of different automated surveillance systems that can detect, track and analyze video sequences both online and offline and report potential security risks. Segmentation of objects is an important part of such systems and numerous background segmentation techniques have been used in the literature. One common challenge faced by these techniques is adaption in different lighting environments. A new improved background segmentation technique has been presented in this where the main focus is to accurately segment potentially important objects by reducing the overall false detection rate. Historic edge maps and tracking results are analyzed for this purpose. The idea is to obtain an up to date edge map of the segmented region highlighted as foreground areas and compare them with the stored results. The edge maps are obtained using a novel adaptive edge orientation based technique where orientation of the edge is used. Experimental results have shown that the discussed technique gives over 85% matching results even in severe lighting changes.