Towards Computational Identification of Visual Attention on Interactive Tabletops
Companion Proceedings of the 2020 Conference on Interactive Surfaces and Spaces, 2020•dl.acm.org
There is a growing interest in the ability to detect where people are looking in real-time to
support learning, collaboration, and efficiency. Here we present an overview of
computational methods for accurately classifying the area of visual attention on a horizontal
surface that we use to represent an interactive display (ie tabletop). We propose a new
model that utilizes a neural network to estimate the area of visual attention, and provide a
close examination of the factors that contribute to the accuracy of the model. Additionally, we …
support learning, collaboration, and efficiency. Here we present an overview of
computational methods for accurately classifying the area of visual attention on a horizontal
surface that we use to represent an interactive display (ie tabletop). We propose a new
model that utilizes a neural network to estimate the area of visual attention, and provide a
close examination of the factors that contribute to the accuracy of the model. Additionally, we …
There is a growing interest in the ability to detect where people are looking in real-time to support learning, collaboration, and efficiency. Here we present an overview of computational methods for accurately classifying the area of visual attention on a horizontal surface that we use to represent an interactive display (i.e. tabletop). We propose a new model that utilizes a neural network to estimate the area of visual attention, and provide a close examination of the factors that contribute to the accuracy of the model. Additionally, we discuss the use of this technique to model joint visual attention in collaboration. We achieved a mean classification accuracy of 75.75% with a standard deviation of 0.14 when data from four participants was used in training the model and then tested on the fifth participant. We also achieved a mean classification accuracy of 98.8% with 0.02 standard deviation when different amounts of overall data was used to test the model.
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