In computer vision, cameras more and more accurate, fast, 3D featured are used. These still evolutions generate more data, which is an issue for users to store it with standard compression for example for recording proof in case of products manufacture defective. The aim of this work is to develop a specific solution adapted for vision systems which have a known scenario and can be described by dynamic models. In this framework, Kalman filters are used for data compression, observable variable prediction, and augmented reality. The developed concepts are tested with a scenario of a ruler on a table. The experiment aims to check the data compression level, the estimation of the friction forces coefficient of the ruler and the prediction of the stop position.