Real-time predictive zoom tracking for digital still cameras |
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Authors: | V Peddigari N Kehtarnavaz |
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Affiliation: | (1) Department of Electrical Engineering, University of Texas at Dallas, P.O. Box 830688, Richardson, TX 75083-0688, USA |
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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.
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Keywords: | Real-time zoom tracking Real-time trace curve tracking Digital still cameras Predictive models Comparison of zoom tracking approaches |
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