Abstract:

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 RPCA, named SCM-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.

Published in: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

DOI: 10.1109/AVSS.2015.7301753

PDF: ResearchGate.net

Keyworks:

double constrained rpca, matrix decomposition, foreground detection

Experimental results:

* Visual results

* Quantitative results

Source code:

* You can find the source code of SCM-RPCA and the related experiments in the following link: https://mega.nz/#F!J99S0boB!tuNy6ezjHVvcdANTR875TQ