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Sensors (Basel). 2016 Nov 24;16(12). pii: E1990.

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks.

Author information

1
Department of Information and Communication Engineering, Daegu-Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea. pjhdrm@dgist.ac.kr.
2
School of Electronics Technology, ITT Technical Institute, Clovis, CA 93612, USA. JRiosRamos@itt-tech.edu.
3
Department of Information and Communication Engineering, Daegu-Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea. tsmoon@dgist.ac.kr.
4
Department of Electrical and Computer Engineering, Lyles College of Engineerning, California State University, Fresno, CA 93740, USA. youngkim@csufresno.edu.

Abstract

Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

KEYWORDS:

aquatic activity classification; convolutional neural networks; micro-Doppler signatures; radar; transfer learning

PMID:
27886151
PMCID:
PMC5190971
DOI:
10.3390/s16121990
[Indexed for MEDLINE]
Free PMC Article

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