Modeling pixel intensities with log-normal distributions for background subtraction
Sotirios Ch. Diamantas, Kostas Alexis · 2017
In this research, the problem of background subtraction is addressed using a single static camera. Aside from the practicality of distinguishing foreground moving objects from background scenes, background subtraction is an essential step towards classifying and tracking objects in complex and dynamic environments. Our proposed method is based on the temporal averaging of individual pixels over a small training sample and the modeling of pixel intensities with a log-normal probability density function that best fits the divergence among background pixels. Our method has been tested in a series of different and challenging environments with illumination changes as well as high speed foreground objects with the view to be used in autonomous vehicles applications for pedestrian and car detection. The results from this research are juxtaposed against the state-of-the-art methods and demonstrate the efficiency of our approach.