Paper Info
Title
Recognition of Activities from Eye Gaze and Egocentric Video.
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.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Histogram,Activity classification,Pattern recognition,Computer science,Convolutional neural network,Feature (computer vision),Eye tracking,Eye movement,Artificial intelligence
DocType
Volume
Citations 
Journal
abs/1805.07253
0
PageRank 
References 
Authors
0.34
10
2
Name
Order
Citations
PageRank
Anjith George1748.79
Aurobinda Routray233752.80