Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

Tyler S. Jordan

doi:10.1117/12.2227947

12 May 2016 Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

Tyler S. Jordan

Proceedings Volume 9825, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XV; 982509 (2016) https://doi.org/10.1117/12.2227947
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Tyler S. Jordan "Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms", 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); https://doi.org/10.1117/12.2227947

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