Saccade control for an active stereo head using a learned spatial representation

An important mechanism in active vision is that of fixating to different targets of interest in a scene. We propose a two stage execution of saccades, where the first stage is an “open-loop” mode based on a learned spatial representation while the second stage is a closed loop “visual servoing” mode. Explicit calibration of the kinematic and imaging parameters of the system is replaced with a self-organized learning scheme, thereby providing a flexible and efficient saccade control strategy. Experiments on the University of Illinois Active Vision System (ULAVS) are used to establish the feasibility of this approach