Paper in ISWC 2015: "Predicting Daily Activities from Egocentric Images Using Deep Learning"


Daniel Castro, Steven Hickson, Vinay Bettadapura, Edison Thomaz, Gregory Abowd, Henrik Christensen, Irfan Essa

Predicting Daily Activities from Egocentric Images Using Deep Learning Inproceedings

In: Proceedings of International Symposium on Wearable Computers (ISWC), 2015.

Abstract | Links | BibTeX | Tags: activity recognition, computer vision, ISWC, machine learning, wearable computing



We present a method to analyze images taken from a passive egocentric wearable camera along with contextual information, such as time and day of the week, to learn and predict the everyday activities of an individual. We collected a dataset of 40,103 egocentric images over 6 months 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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