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Predicting Daily Activities from Egocentric Images using Deep Learning

, , , , , , and . Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, page 75--82. New York, NY, USA, ACM, (2015)
DOI: 10.1145/2802083.2808398

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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