Probability density estimation for object recognition in unmanned aerial vehicle application

Abstract

The problem of object recognition in Unmanned Aerial Vehicle application is considered. Probabilistic Bayesian approach in object recognition is used. The accuracy of object recognition depends directly on the quality of prior data and accuracy of object parameters description. An approach for probability density estimation based of regression model is represented. Probability density functions are estimated by learning samples. The proposed approach is verified by laboratory experiment with video recording of object in rotatable platform.