An Attention Selection System Based on Neural Network and Its Application in Tracking Objects

In this paper an attention selection system based on neural network is proposed, which combines supervised and unsupervised learning reasonably. A value system and memory tree with update ability are regarded as teachers to adjust the weights of neural network. Both bottom-up and top-down part are to simulate two-stage hypothesis of attention selection in biological vision. The system is able to track objects that it is interested in. Whenever it lost focus on tracked object, it can find the object again in a short time.