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arXiv · 1805.07253

Recognition of Activities from Eye Gaze and Egocentric Video

Anjith George·Aurobinda Routray
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Abstract

This paper presents a framework for recognition of human activity from egocentric video and eye tracking data obtained from a head-mounted eye tracker. Three channels of information such as eye movement, ego-motion, and visual features are combined for the classification of activities. Image features were extracted using a pre-trained convolutional neural network. Eye and ego-motion are quantized, and the windowed histograms are used as the features. The combination of features obtains better accuracy for activity classification as compared to individual features.

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Anjith George, Aurobinda Routray. 2018-05-18. Recognition of Activities from Eye Gaze and Egocentric Video. https://arxiv.org/abs/1805.07253

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