14,615 research outputs found
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
How is Gaze Influenced by Image Transformations? Dataset and Model
Data size is the bottleneck for developing deep saliency models, because
collecting eye-movement data is very time consuming and expensive. Most of
current studies on human attention and saliency modeling have used high quality
stereotype stimuli. In real world, however, captured images undergo various
types of transformations. Can we use these transformations to augment existing
saliency datasets? Here, we first create a novel saliency dataset including
fixations of 10 observers over 1900 images degraded by 19 types of
transformations. Second, by analyzing eye movements, we find that observers
look at different locations over transformed versus original images. Third, we
utilize the new data over transformed images, called data augmentation
transformation (DAT), to train deep saliency models. We find that label
preserving DATs with negligible impact on human gaze boost saliency prediction,
whereas some other DATs that severely impact human gaze degrade the
performance. These label preserving valid augmentation transformations provide
a solution to enlarge existing saliency datasets. Finally, we introduce a novel
saliency model based on generative adversarial network (dubbed GazeGAN). A
modified UNet is proposed as the generator of the GazeGAN, which combines
classic skip connections with a novel center-surround connection (CSC), in
order to leverage multi level features. We also propose a histogram loss based
on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in
terms of luminance distribution. Extensive experiments and comparisons over 3
datasets indicate that GazeGAN achieves the best performance in terms of
popular saliency evaluation metrics, and is more robust to various
perturbations. Our code and data are available at:
https://github.com/CZHQuality/Sal-CFS-GAN
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