Predicting Daily Activities From Egocentric Images Using Deep Learning
Abstract
We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.
Keywords
Cite
@article{arxiv.1510.01576,
title = {Predicting Daily Activities From Egocentric Images Using Deep Learning},
author = {Daniel Castro and Steven Hickson and Vinay Bettadapura and Edison Thomaz and Gregory Abowd and Henrik Christensen and Irfan Essa},
journal= {arXiv preprint arXiv:1510.01576},
year = {2015}
}
Comments
8 pages