Dynamic Saliency Models and Human Attention: A Comparative Study on Videos

Riche, Nicolas; Mancaş, Matei; Ćulibrk, Dubravko; Crnojević, Vladimir; Gosselin, Bernard; Dutoit, Thierry

doi:10.1007/978-3-642-37431-9_45

Cited by 35 publications

(26 citation statements)

References 19 publications

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“…The audio was turned off for all videos to isolate the effects of the visual information on attention. The FaceLab eye-tracking device 1 was employed during the experiment by following the practices of the visual attention community [15,33] and those of previous experiment designs investigating visual attention for television watching [9,20,24]. Fig.…”

Section: Apparatusmentioning

confidence: 99%

Evaluating visual attention for multi-screen television: measures, toolkit, and experimental findings

Vatavu

Mancaş

2015

Pers Ubiquit Comput

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New and emerging multi-screen television scenarios and applications need new evaluation procedures, methodologies, and tools to support multi-screen data analysis. In this work, we introduce a set of 12 measures to characterize viewers' visual attention patterns for multiscreen TV. Out of these, eight measures are computed directly from eye-tracking data, while the other four are evaluated using questionnaires. We applied our measures during a controlled experiment involving nine distinct screen layouts with two, three, and four TV screens, for which we report new findings about viewers' distributions of visual attention. For example, we found that viewers need an average discovery time up to 4.5 s to visually fixate four screens, and their subjective perceptions of what they watched and for how long they watched each screen are substantially accurate, i.e., we report Pearson's correlation coefficients up to r = .892 with ground truth measured with eye-tracking equipment. We also analyze and discuss the evolution of our participants' distributions of visual attention over time from the perspective of our new set of measures. For example, we found that people perform significantly more transitions between screens during the first seconds of watching television, after which their level of visual attention converges to a stable value. We complement the findings revealed by our objective eye gaze measurements with subjective data about participants' perceived cognitive work load and comfortability while watching more than one TV screen, and we measure viewers' capacities to understand and recall content delivered simultaneously on multiple screens. To foster new studies and explorations of viewers' visual attention patterns during multi-screen television watching, we release in the community an update for an existing software toolkit (i.e., VATic-TV, the Visual Attention Toolkit for TV, now at version v2) that automatically computes our measures from data delivered by standard eye-tracking equipment. We hope that our new set of measures and the companion software will benefit the community as a first step toward understanding visual attention for emerging multi-screen TV applications and, consequently, will help researchers and practitioners to design new TV applications that will better exploit viewers' visual attention patterns toward new, richer television experiences.

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Section: Apparatusmentioning

confidence: 99%

Evaluating visual attention for multi-screen television: measures, toolkit, and experimental findings

Vatavu

Mancaş

2015

Pers Ubiquit Comput

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“…• Abnormal Surveillance Crowd Moving Noise (AS-CMN) Database [38] comprises eye movements for surveillance-type videos, characterized by abnormal moving objects or camera motion. These eye movements are then employed for evaluating four dynamic saliency models.…”

Section: Video Databasesmentioning

confidence: 99%

Overview of Eye tracking Datasets

Winkler

Subramanian

2013

2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX)

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“…The ASCMN (Abnormal, Surveillance, Crowd, Moving, and Noise) video benchmark [19] is used for evaluation. It is composed of 24 videos separated into 5 categories: Abnormal with surprising motion objects, Surveillance with normal motion objects, Crowd with several crowd densities, Moving with moving camera, and Noise with long period of noise and sudden salient object.…”

Section: Datasetmentioning

confidence: 99%

Spatio-temporal saliency based on rare model

Decombas

Riche

Dufaux

et al. 2013

2013 IEEE International Conference on Image Processing

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In this paper, a new spatio-temporal saliency model is presented. Based on the idea that both spatial and temporal features are needed to determine the saliency of a video, this model builds upon the fact that locally contrasted and globally rare features are salient. The features used in the model are both spatial (color and orientations) and temporal (motion amplitude and direction) at several scales. To be more robust to moving camera a module computes the global motion and to be more consistent in time, the saliency maps are combined together after a temporal filtering. The model is evaluated on a dataset of 24 videos split into 5 categories (Abnormal, Surveillance, Crowds, Moving camera, and Noisy). This model achieves better performance when compared to several state-of-the-art saliency models.

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Dynamic Saliency Models and Human Attention: A Comparative Study on Videos

Cited by 35 publications

References 19 publications

Evaluating visual attention for multi-screen television: measures, toolkit, and experimental findings

Evaluating visual attention for multi-screen television: measures, toolkit, and experimental findings

Overview of Eye tracking Datasets

Spatio-temporal saliency based on rare model

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