Studying Relationships between Human Gaze, Description, and Computer Vision

Yun, Kiwon; Peng, Yifan; Samaras, Dimitris; Zelinsky, Gregory J.; Berg, Tamara L.

doi:10.1109/cvpr.2013.101

Cited by 71 publications

(65 citation statements)

References 30 publications

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“…Yun et al [52] collect eye movement data for a 1,000 image subset of Pascal VOC 2008; three observers performed a three second free-viewing task. This data is then used to to re-rank the output of an object class detector [15] on test images.…”

Section: Related Workmentioning

confidence: 99%

Training Object Class Detectors from Eye Tracking Data

Papadopoulos

Clarke

Keller

et al. 2014

Computer Vision – ECCV 2014

101

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Abstract. Training an object class detector typically requires a large set of images annotated with bounding-boxes, which is expensive and time consuming to create. We propose novel approach to annotate object locations which can substantially reduce annotation time. We first track the eye movements of annotators instructed to find the object and then propose a technique for deriving object bounding-boxes from these fixations. To validate our idea, we collected eye tracking data for the trainval part of 10 object classes of Pascal VOC 2012 (6,270 images, 5 observers). Our technique correctly produces bounding-boxes in 50% of the images, while reducing the total annotation time by factor 6.8× compared to drawing bounding-boxes. Any standard object class detector can be trained on the bounding-boxes predicted by our model. Our large scale eye tracking dataset is available at groups.inf.ed.ac.uk/calvin/eyetrackdataset/.

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Section: Related Workmentioning

confidence: 99%

Training Object Class Detectors from Eye Tracking Data

Papadopoulos

Clarke

Keller

et al. 2014

Computer Vision – ECCV 2014

101

View full text Add to dashboard Cite

show abstract

“…More recently, several algorithmic approaches have been developed that leverage advances in computer vision techniques to support the annotation process (De Beugher, 2012;Toyama, 2012;Yun, 2013). The vision algorithms, such as SIFT and SURF, rely on object dictionaries that can be used to match images in the eye-tracking data (Bay, 2006;Lowe, 2004).…”

Section: Algorithmicmentioning

confidence: 99%

Making Sense of Mobile Eye-Tracking Data in the Real-World

Fong

Ratwani

2016

Proceedings of the Human Factors and Ergonomics Society Annual

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Stationary eye-tracking technology has been used extensively in human-computer interaction to both understand how humans interact with computers and as an interaction mechanism. Mobile eye-tracking technology is becoming more prevalent, yet the analysis and annotation of mobile eye-tracking data remains challenging. We present a novel human-in-the-loop approach for mobile eye-tracking data analysis that dramatically reduces resource requirements. This method incorporates human insight in a semiautomatic decision making process, leveraging both computational power and human decision making abilities. We demonstrate the accuracy of this approach with eye movement data from two real-world use cases. Average accuracy across the two environments is 82.3%. Our approach holds tremendous promise and has the potential to open the door to more robust eye movement studies in the real-world.

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“…For example, egocentric videos have been used to reconstruct 3D social gaze to analyze human-human interaction [15], and eye gaze information has been incorporated into first-person activity recognition [6]. The use of gaze information is also becoming more and more important in computer vision tasks such as object and scene recognition [11,20].…”

Section: Introductionmentioning

confidence: 99%

4th international workshop on pervasive eye tracking and mobile eye-based interaction

Pfeiffer

Stellmach

Sugano

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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Previous work on eye tracking and eye-based human-computer interfaces mainly concentrated on making use of the eyes in traditional desktop settings. With the recent growth of interest in smart glass devices and low-cost eye trackers, however, gaze-based techniques for mobile computing is becoming increasingly important. PETMEI 2014 focuses on the pervasive eye tracking paradigm as a trailblazer for mobile eye-based interaction and eye-based context-awareness. We want to stimulate and explore the creativity of these communities with respect to the implications, key research challenges, and new applications for pervasive eye tracking in ubiquitous computing. The long-term goal is to create a strong interdisciplinary research community linking these fields together and to establish the workshop as the premier forum for research on pervasive eye tracking.

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Studying Relationships between Human Gaze, Description, and Computer Vision

Cited by 71 publications

References 30 publications

Training Object Class Detectors from Eye Tracking Data

Training Object Class Detectors from Eye Tracking Data

Making Sense of Mobile Eye-Tracking Data in the Real-World

4th international workshop on pervasive eye tracking and mobile eye-based interaction

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