Video attention prediction using gaze saliency

Chen, Yanxiang; Tao, Gang; Xie, Qiangqiang; Song, Minglong

doi:10.1007/s11042-016-4294-1

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Bottom-up approaches employ low-level image features, such as intensity, color, and orientation to predict visual attention, while top-down approaches take into consideration high-level features of the scene (such as object/human relationships) and the context. Hybrid approaches combine low-level and high-level features to improve performance, allowing for recent applications to predict video attention during natural interactions of the user with a smartphone [ 86 ]. With advances in deep learning, new saliency-based gaze tracking models are continuously proposed, but they are most exploited to learn salient regions in order to predict eye fixations and then eliminate explicit personal calibration while using PCCR based eye trackers [ 87 ].…”

Section: Gaze Tracking By Scene Analysismentioning

confidence: 99%

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

Cazzato

Leo

Distante

et al. 2020

Sensors

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The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.

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Section: Gaze Tracking By Scene Analysismentioning

confidence: 99%

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

Cazzato

Leo

Distante

et al. 2020

Sensors

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“…Saliency prediction can be categorized into three general categories: bottom-up approaches based on low-level features such as color, contrastness, texture, etc. [7,18,30,[45][46][47]; top-down approaches based on high-level image features such as object knowledge [13,16,38]; and the combination of the two [6,44]. Development in deep learning boosts the performance of saliency prediction, where saliency datasets used for training and benchmark (SALICON [17], MIT300 [4], etc.)…”

Section: Dnns For Saliency Predictionmentioning

confidence: 99%

Where's your focus : personalized attention

Lin¹,

Hui²

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Human visual attention is subjective and biased according to the personal preference of the viewer, however, current works of saliency detection are general and objective, without counting the factor of the observer. This will make the attention prediction for a particular person not accurate enough. In this work, we present the novel idea of personalized attention prediction and develop Personalized Attention Network (PANet), a convolutional network that predicts saliency in images with personal preference. The model consists of two streams which share common feature extraction layers, and one stream is responsible for saliency prediction, while the other is adapted from the detection model and used to fit user preference. We automatically collect user preference from their albums and leaves them freedom to define what and how many categories their preferences are divided into. To train PANet, we dynamically generate ground truth saliency maps upon existing detection labels and saliency labels, and the generation parameters are based upon our collected datasets consists of 1k images. We evaluate the model with saliency prediction metrics and test the trained model on different preference vectors. The results have shown that our system is much better than general models in personalized saliency prediction and is efficient to use for different preferences. CCS CONCEPTS• Computing methodologies → Interest point and salient region detections; • Human-centered computing → Human computer interaction (HCI);

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