A novel framework for video summarization based on smooth pursuit information from eye tracker data

Salehin, Musfequs; Paul, Manoranjan

doi:10.1109/icmew.2017.8026294

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…Since eye gaze is known to provide cues on the underlying cognitive processes [ 11 , 12 ], it can be expected to be particularly useful for this kind of video-processing task. However, despite being used in some computer vision problems [ 13 , 14 , 15 , 16 ], its general use has been limited.…”

Section: Related Workmentioning

confidence: 99%

Glimpse: A Gaze-Based Measure of Temporal Salience

Traver

Zorío

Leiva

2021

Sensors

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Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

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Section: Related Workmentioning

confidence: 99%

Glimpse: A Gaze-Based Measure of Temporal Salience

Traver

Zorío

Leiva

2021

Sensors

View full text Add to dashboard Cite

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“…Segmentation of eye movements into discrete events is an important part of eye movement research and has been investigated for decades. Although we discuss the definitions of the particular eye movement types later in the paper, reliably separating gaze events from one another enables a large number of analyses of eye tracking data sets in order to search for group differences or similarities (Dowiasch et al, 2016;Silberg et al, 2019), find the differences in viewing behavior for different stimulus types (Vig, Dorr, Martinetz, & Barth, 2011), and many other research applications, including media summarisation (Salehin & Paul, 2017).…”

Section: Introductionmentioning

confidence: 99%