Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach

Shan Zhang, Baocheng Geng, Pramod K. Varshney, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose regular vine copula based fusion of multiple deep neural network classifiers for the problem of multi-sensor based human activity recognition. We take the cross-modal dependence into account by employing regular vine copulas that are extremely flexible and powerful graphical models to characterize complex dependence among multiple modalities. Multiple deep neural networks are used to extract high-level features from multi-sensing modalities, with each deep neural network processing the data collected from a single sensor. The extracted high-level features are then combined using a regular vine copula model. Numerical experiments are conducted to demonstrate the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationFUSION 2019 - 22nd International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780996452786
StatePublished - Jul 2019
Event22nd International Conference on Information Fusion, FUSION 2019 - Ottawa, Canada
Duration: Jul 2 2019Jul 5 2019

Publication series

NameFUSION 2019 - 22nd International Conference on Information Fusion

Conference

Conference22nd International Conference on Information Fusion, FUSION 2019
CountryCanada
CityOttawa
Period7/2/197/5/19

ASJC Scopus subject areas

  • Information Systems
  • Instrumentation

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    Zhang, S., Geng, B., Varshney, P. K., & Rangaswamy, M. (2019). Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach. In FUSION 2019 - 22nd International Conference on Information Fusion [9011282] (FUSION 2019 - 22nd International Conference on Information Fusion). Institute of Electrical and Electronics Engineers Inc..