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Classification of human activity on water through micro-Dopplers using deep convolutional neural networks

Title
Classification of human activity on water through micro-Dopplers using deep convolutional neural networks
Authors
Kim, YoungwookMoon, Taesup
Issue Date
2016
Citation
Radar Sensor Technology XX, 9829
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
ISSN
0277-786X
Abstract
Detecting humans and classifying their activities on the water has significant applications for surveillance, border patrols, and rescue operations. When humans are illuminated by radar signal, they produce micro-Doppler signatures due to moving limbs. There has been a number of research into recognizing humans on land by their unique micro-Doppler signatures, but there is scant research into detecting humans on water. In this study, we investigate the micro-Doppler signatures of humans on water, including a swimming person, a swimming person pulling a floating object, and a rowing person in a small boat. The measured swimming styles were free stroke, backstroke, and breaststroke. Each activity was observed to have a unique micro-Doppler signature. Human activities were classified based on their micro-Doppler signatures. For the classification, we propose to apply deep convolutional neural networks (DCNN), a powerful deep learning technique. Rather than using conventional supervised learning that relies on handcrafted features, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms for image recognition, directly to a raw micro-Doppler spectrogram of humans on the water. Without extracting any explicit features from the micro-Dopplers, the DCNN can learn the necessary features and build classification boundaries using the training data. We show that the DCNN can achieve accuracy of more than 87.8% for activity classification using 5- fold cross validation. © 2016 SPIE.
URI
http://hdl.handle.net/20.500.11750/5449
DOI
10.1117/12.2224196
Publisher
SPIE
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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