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A zoom tracking algorithm based on deep learning
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DOI：10.1007/s11042-021-10868-2.png)
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Zoom tracking is an essential and critical technology of digital cameras. Zoom tracking is typically achieved by moving the zoom and focus motors in lenses following the trace curve. The trace curve shows the in-focus motor positions versus the zoom motor positions for a specific object distance. Considering the limitations of the existing methods in responsiveness and accuracy, we propose a new zoom tracking algorithm based on deep learning, DPZT (Deep Predictive Zoom Tracking). The input of DPZT is a two-dimensional vector, consisting of image distance in two specific focus length. The structure of DPZT is a network with three hidden layers, which consists of 30, 30 and 3 neurons of each layer. Our method optimizes the mean offsets to 0.842 steps. With sufficient amount of field experiments, the newly proposed algorithm has proven its superiority as compared to the existing ones. Particularly, the architecture solves the one-to-many map problem at the root cause. Despite the slight increase in the storage capacity, the unprecedented enhancement of tracking accuracy and responsiveness outweighs the side effect.
Keyword:
Real-time zoom tracking
deep learning
trace curve
auto focus
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