title: Adaptable Bayesian classifier for spatiotemporal nonparametric moving object detection strategies.
abstract: 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.
authors.affiliation: ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain. ccr@gti.ssr.upm.es
authors.forename: Carlos
authors.initials: C
authors.lastname: Cuevas
authors.forename: Raúl
authors.initials: R
authors.lastname: Mohedano
authors.forename: Narciso
authors.initials: N
authors.lastname: García