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

Paper

  • D. Castro, S. Hickson, Vinay Bettadapura, E. Thomaz, G. Abowd, H. Christensen, and I. Essa (2015), “Predicting Daily Activities from Egocentric Images Using Deep Learning,” in Proceedings of International Symposium on Wearable Computers (ISWC), 2015. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{ 2015-Castro-PDAFEIUDL,
    arxiv = {http://arxiv.org/abs/1510.01576},
    author  = {Daniel Castro and Steven Hickson and Vinay
    Bettadapura and Edison Thomaz and Gregory Abowd and
    Henrik Christensen and Irfan Essa},
    booktitle  = {Proceedings of International Symposium on Wearable
    Computers (ISWC)},
    month = {September},
    pdf = {http://www.cc.gatech.edu/~irfan/p/2015-Castro-PDAFEIUDL.pdf},
    title = {Predicting Daily Activities from Egocentric Images
    Using Deep Learning},
    url = {http://www.cc.gatech.edu/cpl/projects/dailyactivities/},
    year = {2015}
    }

Abstract

Castro-ISWC2015

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 a 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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