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This paper deals with active tracking of 3D moving targets. Tracking is based on different visual behaviors, namely smooth pursuit and vergence control. The performance and robustness in visual control of motion depends both on the vision algorithms and the control structure. In this work we evaluate these two aspects, characterize the delays, and discuss ways to cope with latency while improving system performance. Kalman filtering is used to achieve smooth behaviors and increase visual processing robustness. A specific Kalman filter structure is proposed and its tuning and initialization are discussed. Delays and system latencies substantially affect the performance of visually guided systems. Interpolation is used to cope with visual processing delays. Model predictive control strategies are proposed to compensate for the mechanical latency in visual control of motion.