<title>General relationship for optimal tracking performance</title>

Abstract

Visual tracking is a vital task in active vision research, traffic surveillance, face following, robotics, and many other applications. This paper investigates the principles of finding optimal tracking performance depending on image tesselation and window size. Square windows reach best performance when image sampling time equals image processing time. This is valid in all cases where the algorithm investigates each pixel in the window and for both tracking with fixed or steered camera/s. Linear windows can improve tracking performance, though performance is limited, too. Best performance yield space-variant image tessellations. Image pyramids or log-polar sampled images show steadily increasing tracking performance with increasing sensor size. The reason is that the resolution drops as sensor size increases.