Fast camera focus estimation for gaze-based focus control
This addresses usability issues in gaze-based autofocus for camera systems, offering a domain-specific incremental improvement.
The paper tackles the problem of slow manual focus selection in cameras by introducing a real-time auto-focus method based on eye-tracking, which enables swift focus plane shifting using gaze information and achieves a runtime of ~20ms on a single i5 core. It evaluates the algorithm against state-of-the-art approaches on eight new datasets and public ones, showing competitive performance.
Many cameras implement auto-focus functionality. However, they typically require the user to manually identify the location to be focused on. While such an approach works for temporally-sparse autofocusing functionality (e.g., photo shooting), it presents extreme usability problems when the focus must be quickly switched between multiple areas (and depths) of interest - e.g., in a gaze-based autofocus approach. This work introduces a novel, real-time auto-focus approach based on eye-tracking, which enables the user to shift the camera focus plane swiftly based solely on the gaze information. Moreover, the proposed approach builds a graph representation of the image to estimate depth plane surfaces and runs in real time (requiring ~20ms on a single i5 core), thus allowing for the depth map estimation to be performed dynamically. We evaluated our algorithm for gaze-based depth estimation against state-of-the-art approaches based on eight new data sets with flat, skewed, and round surfaces, as well as publicly available datasets.