Abstract
Computer vision systems can predict the motion of objects if the movement behavior is analyzed over time, ie, it is possible to find out future values based on previously observed values. In this paper we present an statistical analysis which is aimed to compare two models to predict the position of moving objects in next frames. The models presented are the Kalman filter and an analysis of time series using an ARIMA model. Scenarios with different characteristics are presented as test cases. Segmentation of moving objects is done through the clustering of optical flow vectors for similarity, which are obtained by Pyramid Lucas and Kanade algorithm.
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