  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.
