Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl