Electronic devices endowed with camera platforms require new and powerful machine vision applications, which commonly include moving object detection strategies. To obtain high-quality results, the most recent strategies estimate nonparametrically background and foreground models and combine them by means of a Bayesian classifier. However, typical classifiers are limited by the use of constant prior values and they do not allow the inclusion of additional spatiodependent prior information. In this Letter, we propose an alternative Bayesian classifier that, unlike those reported before, allows the use of additional prior information obtained from any source and depending on the spatial position of each pixel.
© 2012 Optical Society of America
Original Manuscript: April 26, 2012
Revised Manuscript: June 25, 2012
Manuscript Accepted: June 25, 2012
Published: July 25, 2012
Carlos Cuevas, Raúl Mohedano, and Narciso García, "Adaptable Bayesian classifier for spatiotemporal nonparametric moving object detection strategies," Opt. Lett. 37, 3159-3161 (2012)