Novel region-based modeling for human detection within highly dynamic aquatic environment

One difficult challenge in autonomous video surveillance is on handling highly dynamic backgrounds. This difficulty is compounded if foreground objects of interest are partially hidden by specular reflections or glare. In this paper, we provide numerous insights into the technical difficulties faced in developing an automated video surveillance system within a hostile environment: an outdoor public swimming pool. For robust detection performance, we focused on two central aspects: (i) effective modeling of the dynamic outdoor aquatic background with rapid illumination changes, splashes and random spatial movements of background elements, owing to the movement of water ripples; and (ii) enhancing the visibility of swimmers that are partially hidden by specular reflections. Several innovations have been introduced from scratch in this paper. The first is the development of a scheme that models the background as regions of dynamic homogeneous processes. This model facilitates the implementation of an efficient spatial searching scheme for background subtraction that could exploit long-range spatial dependencies between pixels. The second is the implementation of a spatio-temporal filtering scheme that enhances the detection of swimmers that are partially hidden by specular reflections of artificial nighttime lighting, serving as a pre-processing module to foreground detection for nighttime operation. These various algorithms have been tightly integrated under a unified framework and demonstrated on a busy Olympic-sized outdoor public pool.