Multiple target tracking in clutter backgrounds using self-organizing feature map

Target tracking in a real world situation is a difficult problem because of continuous variations in images, huge amount of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of the differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neuron positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.