  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.
