Fast camera focus estimation for gaze-based focus control
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2017.
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Citing papers per year: 2021: 6; 2022: 2; 2023: 2.
10 papers.
- 2021TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Typescited by 32
- 2021The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applicationscited by 10
- 2021A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysiscited by 7
- 20211000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learningcited by 7
- 2021Maximum and Leaky Maximum Propagationcited by 4
- 2021Tensor Normalization and Full Distribution Trainingcited by 3
- 2023One step closer to EEG based eye trackingcited by 3
- 2022GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projectioncited by 2
- 2022Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnetscited by 0
- 2023A temporally quantized distribution of pupil diameters as a new feature for cognitive load classificationcited by 0
- 2023A temporally quantized distribution of pupil diameters as a new feature for cognitive load classification3 Mar 2023
- 2023One step closer to EEG based eye tracking3 Mar 2023
- 2022Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets20 Jun 2022
- 2022GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projection19 Jan 2022
- 2021Tensor Normalization and Full Distribution Training6 Sep 2021
- 2021Maximum and Leaky Maximum Propagation21 May 2021
- 2021TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types3 Feb 2021
- 20211000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning3 Feb 2021
- 2021A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis12 Jan 2021
- 2021The Gaze and Mouse Signal as additional Source for User Fingerprints in Browser Applications11 Jan 2021