On optimising tracking performance for visual servoing

Visual tracking is a fundamental primitive in advanced sensing tasks, such as vision-guided manipulation or surveillance. We investigate the influence of window size and image tessellation on tracking performance and show that optimal performance for uniform image tesselations is obtained when image sampling time equals image processing time. The performance measures cover velocity, acceleration, and jerk by utilizing different types of feature prediction. We then show that one-dimensional windows can improve performance for specific targets. Finally we show that multi-resolution approaches (log-polar and image pyramid) greatly improve tracking performance.