@inproceedings{steil18_etra,
title = {Fixation Detection for Head-Mounted Eye Tracking Based on Visual Similarity of Gaze Targets},
author = {Julian Steil and Michael Xuelin Huang and Andreas Bulling},
url = {https://perceptual.mpi-inf.mpg.de/files/2018/04/steil18_etra.pdf
https://perceptual.mpi-inf.mpg.de/research/datasets/#steil18_etra},
doi = {10.1145/3204493.3204538},
year = {2018},
date = {2018-03-28},
booktitle = {Proc. International Symposium on Eye Tracking Research and Applications (ETRA)},
pages = {23:1-23:9},
abstract = {Fixations are widely analysed in human vision, gaze-based interaction, and experimental psychology research. However, robust fixation detection in mobile settings is profoundly challenging given the prevalence of user and gaze target motion. These movements feign a shift in gaze estimates in the frame of reference defined by the eye tracker's scene camera. To address this challenge, we present a novel fixation detection method for head-mounted eye trackers. Our method exploits that, independent of user or gaze target motion, target appearance remains about the same during a fixation. It extracts image information from small regions around the current gaze position and analyses the appearance similarity of these gaze patches across video frames to detect fixations. We evaluate our method using fine-grained fixation annotations on a five-participant indoor dataset (MPIIEgoFixation) with more than 2,300 fixations in total. Our method outperforms commonly used velocity- and dispersion-based algorithms, which highlights its significant potential to analyse scene image information for eye movement detection.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@techreport{steil2018_arxiv2,
title = {Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors},
author = {Julian Steil and Philipp Müller and Yusuke Sugano and Andreas Bulling},
url = {https://arxiv.org/abs/1801.06011
https://perceptual.mpi-inf.mpg.de/files/2018/01/steil2018_arxiv2.pdf},
year = {2018},
date = {2018-01-18},
abstract = {Users' visual attention is highly fragmented during mobile interactions but the erratic nature of these attention shifts currently limits attentive user interfaces to adapt after the fact, i.e. after shifts have already happened, thereby severely limiting the adaptation capabilities and user experience. To address these limitations, we study attention forecasting -- the challenging task of predicting whether users' overt visual attention (gaze) will shift between a mobile device and environment in the near future or how long users' attention will stay in a given location. To facilitate the development and evaluation of methods for attention forecasting, we present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). As a first step towards a fully-fledged attention forecasting interface, we further propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate the feasibility of forecasting bidirectional attention shifts between the device and the environment as well as for predicting the first and total attention span on the device and environment using our method. We further study the impact of different sensors and feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions. },
type = {arXiv:1801.06011},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}

@techreport{steil2018_arxiv,
title = {PrivacEye: Privacy-Preserving First-Person Vision Using Image Features and Eye Movement Analysis},
author = {Julian Steil and Marion Koelle and Wilko Heuten and Susanne Boll and Andreas Bulling},
url = {https://arxiv.org/abs/1801.04457
https://perceptual.mpi-inf.mpg.de/files/2018/01/steil2018_arxiv.pdf},
year = {2018},
date = {2018-01-13},
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. },
type = {arXiv:1801.04457},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}

@article{tonsen17_imwut,
title = {InvisibleEye: Mobile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation},
author = {Marc Tonsen and Julian Steil and Yusuke Sugano and Andreas Bulling},
url = {https://perceptual.mpi-inf.mpg.de/files/2017/08/tonsen17_imwut.pdf
https://perceptual.mpi-inf.mpg.de/research/datasets/#tonsen17_imwut},
doi = {10.1145/3130971},
year = {2017},
date = {2017-07-24},
journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
volume = {1},
number = {3},
pages = {106:1-106:21},
abstract = {Analysis of everyday human gaze behaviour has significant potential for ubiquitous computing, as evidenced by a large body of work in gaze-based human-computer interaction, attentive user interfaces, and eye-based user modelling. However, current mobile eye trackers are still obtrusive, which not only makes them uncomfortable to wear and socially unacceptable in daily life, but also prevents them from being widely adopted in the social and behavioural sciences. To address these challenges we present InvisibleEye, a novel approach for mobile eye tracking that uses millimetre-size RGB cameras that can be fully embedded into normal glasses frames. To compensate for the cameras’ low image resolution of only a few pixels, our approach uses multiple cameras to capture different views of the eye, as well as learning-based gaze estimation to directly regress from eye images to gaze directions. We prototypically implement our system and characterise its performance on three large-scale, increasingly realistic, and thus challenging datasets: 1) eye images synthesised using a recent computer graphics eye region model, 2) real eye images recorded of 17 participants under controlled lighting, and 3) eye images recorded of four participants over the course of four recording sessions in a mobile setting. We show that InvisibleEye achieves a top person-specific gaze estimation accuracy of 1.79° using four cameras with a resolution of only 5×5 pixels. Our evaluations not only demonstrate the feasibility of this novel approach but, more importantly, underline its significant potential for finally realising the vision of invisible mobile eye tracking and pervasive attentive user interfaces.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@techreport{mansouryar16_arxiv,
title = {3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers},
author = {Mohsen Mansouryar and Julian Steil and Yusuke Sugano and Andreas Bulling
},
url = {http://arxiv.org/abs/1601.02644
https://perceptual.mpi-inf.mpg.de/files/2016/01/Mansouryar16_arxiv.pdf},
year = {2016},
date = {2016-01-11},
abstract = {3D gaze information is important for scene-centric attention analysis but accurate estimation and analysis of 3D gaze in real-world environments remains challenging. We present a novel 3D gaze estimation method for monocular head-mounted eye trackers. In contrast to previous work, our method does not aim to infer 3D eyeball poses but directly maps 2D pupil positions to 3D gaze directions in scene camera coordinate space. We first provide a detailed discussion of the 3D gaze estimation task and summarize different methods, including our own. We then evaluate the performance of different 3D gaze estimation approaches using both simulated and real data. Through experimental validation, we demonstrate the effectiveness of our method in reducing parallax error, and we identify research challenges for the design of 3D calibration procedures. },
type = {arXiv:1601.02644},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}

@inproceedings{Steil_Ubicomp15,
title = {Discovery of Everyday Human Activities From Long-Term Visual Behaviour Using Topic Models},
author = {Julian Steil and Andreas Bulling},
url = {https://perceptual.mpi-inf.mpg.de/files/2015/08/Steil_Ubicomp15.pdf
https://perceptual.mpi-inf.mpg.de/research/datasets/#steil15_ubicomp},
doi = {10.1145/2750858.2807520},
year = {2015},
date = {2015-05-21},
booktitle = {Proc. of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015)},
pages = {75-85},
abstract = {Human visual behaviour has significant potential for activity recognition and computational behaviour analysis, but previous works focused on supervised methods and recognition of predefined activity classes based on short-term eye movement recordings. We propose a fully unsupervised method to discover users' everyday activities from their long-term visual behaviour. Our method combines a bag-of-words representation of visual behaviour that encodes saccades, fixations, and blinks with a latent Dirichlet allocation (LDA) topic model.
We further propose different methods to encode saccades for their use in the topic model. We evaluate our method on a novel long-term gaze dataset that contains full-day recordings of natural visual behaviour
of 10 participants (more than 80 hours in total). We also provide annotations for eight sample activity classes (outdoor, social interaction, focused work, travel, reading, computer work, watching media, eating) and periods with no specific activity. We show the ability of our method to discover these activities with performance competitive with that of previously published supervised methods.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}