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Proceedings Paper

Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms
Author(s): Tyler S. Jordan
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Paper Abstract

This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

Paper Details

Date Published: 12 May 2016
PDF: 9 pages
Proc. SPIE 9825, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XV, 982509 (12 May 2016); doi: 10.1117/12.2227947
Show Author Affiliations
Tyler S. Jordan, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 9825:
Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XV
Edward M. Carapezza, Editor(s)

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