scispace - formally typeset
Topic

Eye tracking

About: Eye tracking is a(n) research topic. Over the lifetime, 17102 publication(s) have been published within this topic receiving 370872 citation(s). The topic is also known as: eyetracking.
Papers
More filters

Journal ArticleDOI
TL;DR: The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined.
Abstract: Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

6,131 citations


Book
01 Sep 1967-

3,471 citations


Journal ArticleDOI
TL;DR: A tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target, and includes a method for correctly updating the sample mean and a forgetting factor to ensure less modeling power is expended fitting older observations.
Abstract: Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object's appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.

3,016 citations


Book
22 Dec 2012-
TL;DR: To the Human Visual System (HVS), Visual Attention, Neurological Substrate of the HVS, and Neuroscience and Psychology, and Industrial Engineering and Human Factors.
Abstract: to the Human Visual System (HVS).- Visual Attention.- Neurological Substrate of the HVS.- Visual Psychophysics.- Taxonomy and Models of Eye Movements.- Eye Tracking Systems.- Eye Tracking Techniques.- Head-Mounted System Hardware Installation.- Head-Mounted System Software Development.- Head-Mounted System Calibration.- Table-Mounted System Hardware Installation.- Table-Mounted System Software Development.- Table-Mounted System Calibration.- Eye Movement Analysis.- Eye Tracking Methodology.- Experimental Design.- Suggested Empirical Guidelines.- Case Studies.- Eye Tracking Applications.- Diversity and Types of Eye Tracking Applications.- Neuroscience and Psychology.- Industrial Engineering and Human Factors.- Marketing/Advertising.- Computer Science.- Conclusion.

2,251 citations


Journal ArticleDOI
TL;DR: It is shown that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks.
Abstract: In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called “tracking by detection” has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.

2,002 citations


9


Network Information
Related Topics (5)
Facial expression

17K papers, 639.9K citations

91% related
Fixation (psychology)

242 papers, 9.1K citations

89% related
Visual perception

20.8K papers, 997.2K citations

89% related
Cognitive neuroscience of visual object recognition

13.6K papers, 622.2K citations

89% related
Facial recognition system

38.7K papers, 883.4K citations

88% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202210
20211,113
20201,461
20191,527
20181,491
20171,343

Top Attributes

Show by:

Topic's top 5 most impactful authors

Andreas Bulling

97 papers, 4.7K citations

Andrew T. Duchowski

74 papers, 5.3K citations

Enkelejda Kasneci

71 papers, 1.1K citations

Jeff B. Pelz

41 papers, 548 citations

Kenneth Holmqvist

33 papers, 852 citations