Real-time predictive zoom tracking for digital still cameras

Abstract

Zoom tracking is becoming a standard feature in digital still cameras (DSCs). It involves keeping an object of interest in focus during the zooming-in or zooming-out operation. Zoom tracking is normally achieved by moving the focus motor in real-time according to the so-called trace curves in response to changes in the zoom motor position. A trace curve denotes in-focus motor positions versus zoom motor positions for a specific object distance. A zoom tracking approach is characterized by the way these trace curves are estimated and followed. In this paper, a new zoom tracking approach, named predictive zoom tracking (PZT), is introduced based on two prediction models: auto-regressive and recurrent neural network. The performance of this approach is compared with the existing zoom tracking approaches commonly used in DSCs. The real-time implementation results obtained on an actual digital camera platform indicate that the developed PZT approach not only achieves higher tracking accuracies but also effectively addresses the key challenge of zoom tracking, namely the one-to-many mapping problem.