Title:
Predicting Daily Activities From Egocentric Images Using Deep Learning

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Castro, Daniel
Hickson, Steven
Bettadapura, Vinay
Thomaz, Edison
Abowd, Gregory D.
Christensen, Henrik I.
Essa, Irfan
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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.
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Date Issued
2015
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