Neural structures for visual motion tracking

This paper addresses visual motion tracking by a connectionist method, and aims at showing how the flexibility and the generalization power of neural networks can enhance a tracking system's adaptiveness and effectiveness. The simple principle of operation widens the range of applicability. A set of tracking structures that exhibit increasing levels of integration and efficiency are described. We also show how multinetwork architectures for estimate averaging may greatly increase tracking stability. The validity of the basic mechanism was assessed on a simple domain; however, a specific difficult testbed made it possible to verify the effectiveness of the method.