Influence of zoom selection on a Kalman filter

Abstract

The use of a single camera with a zoom lens for tracking involves a continuous arbitration of accuracy vs. reliability. We address this problem with an information-theoretic approach, where we extend zoom selection based on conditional entropy by incorporating the fixation errors into the observation likelihood. We present a thorough analysis of previous approaches, revealing zoom and speed limits, especially how the ratio of process to measurement noise effectively limits the maximally usable zoom for any system tracking with a Kalman filter. This work finally presents means to circumvent aforementioned limitations.