Background modeling is a well-know approach to detect moving objects in video sequences. In recent years, background modeling methods that adopt spatial and texture information have been developed for dealing with complex scenarios. However, none of the investigated approaches have been tested under extreme conditions, such as the underwater domain, on which effects compromising the video quality affect negatively the performance of the background modeling process. In order to overcome such difficulties, more significant features and more robust methods must be found. In this paper, we present a kernel density estimation method which models background and foreground by exploiting textons to describe textures within small and low contrasted regions. Comparison with other texture descriptors, namely, local binary pattern (LBP) and scale invariant local ternary pattern (SILTP) shown improved performance. Besides, quantitative and qualitative performance evaluation carried out on three standard datasets showing very complex conditions revealed that our method outperformed state-of-the-art methods that use different features and modeling techniques and, most importantly, it is able to generalize over different scenarios and targets. 
A texton-based kernel density estimation approach for background modeling under extreme conditions