Tracking and prediction of motion of segmented regions using the Kalman filter

Abstract

Currently many applications require tracking moving objects through a sequence of images. However, sometimes we do not know the characteristics of the movement and even the objects that we will track. In this paper, a complete model for the description and inference of motion of segmented regions is presented, using the Kalman filter without requiring a priori information the scene. Three 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.