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Algorithms for moving object tracking

A new Radial Basis Functions neural network whose training is based on marginal median has been developed which improves motion representation and modelling for dynamic scene understanding.

Motion representation and modelling is an important step towards dynamic image understanding An algorithm for simultaneous estimation and segmentation of the optical flow has been implemented. In this approach, the moving scene is decomposed in different regions with respect to their motion, by means of a Radial Basis Functions (RBF) Neural Network. The learning algorithm for the RBF network employs the median algorithm for estimating the centres of the Radial Basis Functions and the Median of Absolute Deviations (MAD) for estimating their variances. The proposed algorithm has been applied in real image sequences and has been compared to other competitive algorithms (eg. the Iterated Conditional Modes). It has been found that it outperforms the other algorithms.

Reported by

Aristotle University of Thessaloniki
University Campus
54006 Thessaloniki