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.
@article{arxiv.1805.07253,
title = {Recognition of Activities from Eye Gaze and Egocentric Video},
author = {Anjith George and Aurobinda Routray},
journal= {arXiv preprint arXiv:1805.07253},
year = {2018}
}