A multiprocessor-oriented visual tracking system

The design and prototypal realization of a visual tracking system is presented. The approach to target identification is nonconventional, in that it relies on an architecture composed of multiple standard neural networks (multilayer perceptrons) and exploits the information contained in simple features extracted from images, performing a small number of operations. Therefore, the tracking functions are learned by examples, rather than implemented directly. The system demonstrates that a quite complex task such as visual target tracking can be easily obtained by a suitable neural architecture. The fast tracking algorithm and the parallel structure allow a true real-time operation. The system exploits a two-level neural-network hierarchy with a number of parallel networks and an “arbiter”. The training set consists of various geometrical shapes, preprocessed to yield the data vectors. The experimental hardware implementation is based on multiple processing units, implementing the neural architecture, and serves as a prototype for the analysis of the system in practice. A small-sized realization can also be obtained