Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillance

The development of automated video-surveillance applications for maritime environment is a very difficult task due to the complexity of the scenes: moving water, waves, etc. The motion of the objects of interest (i.e. ships or boats) can be mixed with the dynamic behavior of the background (non-regular patterns). In this paper, a double-constrained Robust Principal Component Analysis (RPCA), named SCM-RPCA (Shape and Confidence Map-based RPCA), is proposed to improve the object foreground detection in maritime scenes. The sparse component is constrained by shape and confidence maps both extracted from spatial saliency maps. The experimental results in the UCSD and MarDT data sets indicate a better enhancement of the object foreground mask when compared with some related RPCA methods.