Julian Steil (PhD Student)

MSc Julian Steil

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus
Location
-
Phone
+49 681 9325 2000
Fax
+49 681 9325 2099
Email
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Personal Information

Education

I hold a Bachelor’s degree in Computer Science from Saarland University in February 2013. Instead of continuing the Master’s program in Computer Science I specialized in the fields of image acquisition, analysis and synthesis requiring profound scientific knowledge, in particular in computer science, mathematics, physics, engineering and cognitive science. In December 2014 I received an interdisciplinary Master’s degree in Visual Computing from Saarland University and started my Phd in the Perceptual User Interfaces group.

 

Research Interests

  • Mobile Eye Tracking
  • Image Processing and Computer Vision
  • Machine Learning and Pattern Recognition
  • Human-Computer Interaction

Publications

Steil, J., Koelle, M., Heuten, W., Boll, S., & Bulling, A. (2019). PrivacEye: Privacy-Preserving Head-Mounted Eye Tracking Using Egocentric Scene Image and Eye Movement Features. In Proceedings ETRA 2019. Denver, CO, USA: ACM. doi:10.1145/3314111.3319913
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BibTeX
@inproceedings{steil19_etra, TITLE = {{PrivacEye}: {P}rivacy-Preserving Head-Mounted Eye Tracking UsingEgocentric Scene Image and Eye Movement Features}, AUTHOR = {Steil, Julian and Koelle, Marion and Heuten, Wilko and Boll, Susanne and Bulling, Andreas}, LANGUAGE = {eng}, ISBN = {978-1-4503-6709-7}, DOI = {10.1145/3314111.3319913}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Proceedings ETRA 2019}, EDITOR = {Krejtz, Krzysztof and Sharif, Bonita}, EID = {26}, ADDRESS = {Denver, CO, USA}, }
Endnote
%0 Conference Proceedings %A Steil, Julian %A Koelle, Marion %A Heuten, Wilko %A Boll, Susanne %A Bulling, Andreas %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations External Organizations %T PrivacEye: Privacy-Preserving Head-Mounted Eye Tracking Using Egocentric Scene Image and Eye Movement Features : %G eng %U http://hdl.handle.net/21.11116/0000-0003-2BC0-4 %R 10.1145/3314111.3319913 %D 2019 %B 11th ACM Symposium on Eye Tracking Research & Applications %Z date of event: 2019-06-25 - 2019-06-28 %C Denver, CO, USA %B Proceedings ETRA 2019 %E Krejtz, Krzysztof; Sharif, Bonita %Z sequence number: 26 %I ACM %@ 978-1-4503-6709-7
Steil, J., Hagestedt, I., Huang, M. X., & Bulling, A. (2019). Privacy-Aware Eye Tracking Using Differential Privacy. In Proceedings ETRA 2019. Denver, CO, USA: ACM. doi:10.1145/3314111.3319915
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BibTeX
@inproceedings{steil19_etra2, TITLE = {Privacy-Aware Eye Tracking Using Differential Privacy}, AUTHOR = {Steil, Julian and Hagestedt, Inken and Huang, Michael Xuelin and Bulling, Andreas}, LANGUAGE = {eng}, ISBN = {978-1-4503-6709-7}, DOI = {10.1145/3314111.3319915}, PUBLISHER = {ACM}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, DATE = {2019}, BOOKTITLE = {Proceedings ETRA 2019}, EDITOR = {Krejtz, Krzysztof and Sharif, Bonita}, EID = {27}, ADDRESS = {Denver, CO, USA}, }
Endnote
%0 Conference Proceedings %A Steil, Julian %A Hagestedt, Inken %A Huang, Michael Xuelin %A Bulling, Andreas %+ Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society External Organizations %T Privacy-Aware Eye Tracking Using Differential Privacy : %G eng %U http://hdl.handle.net/21.11116/0000-0003-2BCC-8 %R 10.1145/3314111.3319915 %D 2019 %B 11th ACM Symposium on Eye Tracking Research & Applications %Z date of event: 2019-06-25 - 2019-06-28 %C Denver, CO, USA %B Proceedings ETRA 2019 %E Krejtz, Krzysztof; Sharif, Bonita %Z sequence number: 27 %I ACM %@ 978-1-4503-6709-7
Steil, J., Müller, P., Sugano, Y., & Bulling, A. (2018). Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors. In MobileHCI 2018, 20th International Conference on Human-Computer Interaction with Mobile Devices and Services. Barcelona, Spain: ACM. doi:10.1145/3229434.3229439
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BibTeX
@inproceedings{steil_MobileHCI2018, TITLE = {Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors}, AUTHOR = {Steil, Julian and M{\"u}ller, Philipp and Sugano, Yusuke and Bulling, Andreas}, LANGUAGE = {eng}, ISBN = {978-1-4503-5898-9}, DOI = {10.1145/3229434.3229439}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {MobileHCI 2018, 20th International Conference on Human-Computer Interaction with Mobile Devices and Services}, PAGES = {1--13}, EID = {1}, ADDRESS = {Barcelona, Spain}, }
Endnote
%0 Conference Proceedings %A Steil, Julian %A Müller, Philipp %A Sugano, Yusuke %A Bulling, Andreas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1834-A %R 10.1145/3229434.3229439 %D 2018 %B 20th International Conference on Human-Computer Interaction with Mobile Devices and Services %Z date of event: 2018-09-03 - 2018-09-06 %C Barcelona, Spain %B MobileHCI 2018 %P 1 - 13 %Z sequence number: 1 %I ACM %@ 978-1-4503-5898-9
Steil, J., Huang, M. X., & Bulling, A. (2018). Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets. In Proceedings ETRA 2018. Warsaw, Poland: ACM. doi:10.1145/3204493.3204538
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@inproceedings{steil18_etra, TITLE = {Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets}, AUTHOR = {Steil, Julian and Huang, Michael Xuelin and Bulling, Andreas}, LANGUAGE = {eng}, ISBN = {978-1-4503-5706-7}, DOI = {10.1145/3204493.3204538}, PUBLISHER = {ACM}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, DATE = {2018}, BOOKTITLE = {Proceedings ETRA 2018}, PAGES = {1--9}, EID = {23}, ADDRESS = {Warsaw, Poland}, }
Endnote
%0 Conference Proceedings %A Steil, Julian %A Huang, Michael Xuelin %A Bulling, Andreas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1DAC-E %R 10.1145/3204493.3204538 %D 2018 %B ACM Symposium on Eye Tracking Research & Applications %Z date of event: 2018-06-14 - 2018-06-17 %C Warsaw, Poland %B Proceedings ETRA 2018 %P 1 - 9 %Z sequence number: 23 %I ACM %@ 978-1-4503-5706-7
Steil, J., Koelle, M., Heuten, W., Boll, S., & Bulling, A. (2018). PrivacEye: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis. Retrieved from http://arxiv.org/abs/1801.04457
(arXiv: 1801.04457)
Abstract
As first-person cameras in head-mounted displays become increasingly prevalent, so does the problem of infringing user and bystander privacy. To address this challenge, we present PrivacEye, a proof-of-concept system that detects privacysensitive everyday situations and automatically enables and disables the first-person camera using a mechanical shutter. To close the shutter, PrivacEye detects sensitive situations from first-person camera videos using an end-to-end deep-learning model. To open the shutter without visual input, PrivacEye uses a separate, smaller eye camera to detect changes in users' eye movements to gauge changes in the "privacy level" of the current situation. We evaluate PrivacEye on a dataset of first-person videos recorded in the daily life of 17 participants that they annotated with privacy sensitivity levels. We discuss the strengths and weaknesses of our proof-of-concept system based on a quantitative technical evaluation as well as qualitative insights from semi-structured interviews.
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BibTeX
@online{steil2018_arxiv, TITLE = {{PrivacEye}: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis}, AUTHOR = {Steil, Julian and Koelle, Marion and Heuten, Wilko and Boll, Susanne and Bulling, Andreas}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1801.04457}, EPRINT = {1801.04457}, EPRINTTYPE = {arXiv}, YEAR = {2018}, MARGINALMARK = {$\bullet$}, ABSTRACT = {As first-person cameras in head-mounted displays become increasingly prevalent, so does the problem of infringing user and bystander privacy. To address this challenge, we present PrivacEye, a proof-of-concept system that detects privacysensitive everyday situations and automatically enables and disables the first-person camera using a mechanical shutter. To close the shutter, PrivacEye detects sensitive situations from first-person camera videos using an end-to-end deep-learning model. To open the shutter without visual input, PrivacEye uses a separate, smaller eye camera to detect changes in users' eye movements to gauge changes in the "privacy level" of the current situation. We evaluate PrivacEye on a dataset of first-person videos recorded in the daily life of 17 participants that they annotated with privacy sensitivity levels. We discuss the strengths and weaknesses of our proof-of-concept system based on a quantitative technical evaluation as well as qualitative insights from semi-structured interviews.}, }
Endnote
%0 Report %A Steil, Julian %A Koelle, Marion %A Heuten, Wilko %A Boll, Susanne %A Bulling, Andreas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations External Organizations External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T PrivacEye: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis : %G eng %U http://hdl.handle.net/21.11116/0000-0001-1840-C %U http://arxiv.org/abs/1801.04457 %D 2018 %X As first-person cameras in head-mounted displays become increasingly prevalent, so does the problem of infringing user and bystander privacy. To address this challenge, we present PrivacEye, a proof-of-concept system that detects privacysensitive everyday situations and automatically enables and disables the first-person camera using a mechanical shutter. To close the shutter, PrivacEye detects sensitive situations from first-person camera videos using an end-to-end deep-learning model. To open the shutter without visual input, PrivacEye uses a separate, smaller eye camera to detect changes in users' eye movements to gauge changes in the "privacy level" of the current situation. We evaluate PrivacEye on a dataset of first-person videos recorded in the daily life of 17 participants that they annotated with privacy sensitivity levels. We discuss the strengths and weaknesses of our proof-of-concept system based on a quantitative technical evaluation as well as qualitative insights from semi-structured interviews. %K Computer Science, Human-Computer Interaction, cs.HC
Tonsen, M., Steil, J., Sugano, Y., & Bulling, A. (2017). InvisibleEye: Mobile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(3). doi:10.1145/3130971
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@article{tonsen17_imwut, TITLE = {{InvisibleEye}: {M}obile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation}, AUTHOR = {Tonsen, Marc and Steil, Julian and Sugano, Yusuke and Bulling, Andreas}, LANGUAGE = {eng}, ISSN = {2474-9567}, DOI = {10.1145/3130971}, PUBLISHER = {ACM}, ADDRESS = {New York, NY}, YEAR = {2017}, MARGINALMARK = {$\bullet$}, JOURNAL = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, VOLUME = {1}, NUMBER = {3}, EID = {106}, }
Endnote
%0 Journal Article %A Tonsen, Marc %A Steil, Julian %A Sugano, Yusuke %A Bulling, Andreas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society External Organizations Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T InvisibleEye: Mobile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation : %G eng %U http://hdl.handle.net/11858/00-001M-0000-002D-D0F4-A %R 10.1145/3130971 %7 2017 %D 2017 %J Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies %O IMWUT %V 1 %N 3 %Z sequence number: 106 %I ACM %C New York, NY %@ false
Mansouryar, M., Steil, J., Sugano, Y., & Bulling, A. (2016). 3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers. In Proceedings ETRA 2016. Charleston, SC, USA: ACM. doi:10.1145/2857491.2857530
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@inproceedings{mansouryar16_etra, TITLE = {{3D} Gaze Estimation from {2D} Pupil Positions on Monocular Head-Mounted Eye Trackers}, AUTHOR = {Mansouryar, Mohsen and Steil, Julian and Sugano, Yusuke and Bulling, Andreas}, LANGUAGE = {eng}, ISBN = {978-1-4503-4125-7}, DOI = {10.1145/2857491.2857530}, PUBLISHER = {ACM}, YEAR = {2016}, DATE = {2016}, BOOKTITLE = {Proceedings ETRA 2016}, PAGES = {197--200}, ADDRESS = {Charleston, SC, USA}, }
Endnote
%0 Conference Proceedings %A Mansouryar, Mohsen %A Steil, Julian %A Sugano, Yusuke %A Bulling, Andreas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T 3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-D2CB-2 %R 10.1145/2857491.2857530 %D 2016 %B ACM Symposium on Eye Tracking Research & Applications %Z date of event: 2016-03-14 - 2016-03-17 %C Charleston, SC, USA %B Proceedings ETRA 2016 %P 197 - 200 %I ACM %@ 978-1-4503-4125-7
Steil, J., & Bulling, A. (2015). Discovery of Everyday Human Activities From Long-Term Visual Behaviour Using Topic Models. In UbiComp 2015, ACM International Joint Conference on Pervasive and Ubiquitous Computing. Osaka, Japan: ACM. doi:10.1145/2750858.2807520
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@inproceedings{Steil15_ubicomp, TITLE = {Discovery of Everyday Human Activities From Long-Term Visual Behaviour Using Topic Models}, AUTHOR = {Steil, Julian and Bulling, Andreas}, LANGUAGE = {eng}, ISBN = {978-1-4503-3574-4}, DOI = {10.1145/2750858.2807520}, PUBLISHER = {ACM}, YEAR = {2015}, DATE = {2015}, BOOKTITLE = {UbiComp 2015, ACM International Joint Conference on Pervasive and Ubiquitous Computing}, PAGES = {75--85}, ADDRESS = {Osaka, Japan}, }
Endnote
%0 Conference Proceedings %A Steil, Julian %A Bulling, Andreas %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Discovery of Everyday Human Activities From Long-Term Visual Behaviour Using Topic Models : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0029-5964-1 %R 10.1145/2750858.2807520 %D 2015 %B ACM International Joint Conference on Pervasive and Ubiquitous Computing %Z date of event: 2015-09-07 - 2015-09-11 %C Osaka, Japan %B UbiComp 2015 %P 75 - 85 %I ACM %@ 978-1-4503-3574-4
Steil, J. (2014). Discovery of Eye Movement Patterns in Long-term Visual Behaviour Using Topic Models. Universität des Saarlandes, Saarbrücken.
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@mastersthesis{SteilMaster2014, TITLE = {Discovery of Eye Movement Patterns in Long-term Visual Behaviour Using Topic Models}, AUTHOR = {Steil, Julian}, LANGUAGE = {eng}, SCHOOL = {Universit{\"a}t des Saarlandes}, ADDRESS = {Saarbr{\"u}cken}, YEAR = {2014}, DATE = {2014}, }
Endnote
%0 Thesis %A Steil, Julian %Y Bulling, Andreas %A referee: Fritz, Mario %+ Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society %T Discovery of Eye Movement Patterns in Long-term Visual Behaviour Using Topic Models : %G eng %U http://hdl.handle.net/11858/00-001M-0000-0024-547F-E %I Universität des Saarlandes %C Saarbrücken %D 2014 %P XII, 126 p. %V master %9 master