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

Title
Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks
Authors
Park, J[Park, Jinhee]Javier, RJ[Javier, Rios Jesus]Moon, T[Moon, Taesup]Kim, Y[Kim, Youngwook]
DGIST Authors
Park, J[Park, Jinhee]; Moon, T[Moon, Taesup]
Issue Date
2016-12
Citation
Sensors, 16(12)
Type
Article
Article Type
Article
Keywords
Activity ClassificationsAquatic Activity ClassificationClassification AccuracyClassification of ActivityConvolutionConvolutional Neural NetworkConvolutional Neural NetworksMicro-DopplerMicro-Doppler SignaturesNeural NetworksRadarRadar Cross SectionReal MeasurementsSimulation StudiesTransfer Learning
ISSN
1424-8220
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. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/2109
DOI
10.3390/s16121990
Publisher
MDPI AG
Files:
There are no files associated with this item.
Collection:
Information and Communication EngineeringMachine Learning and Data Science Lab1. Journal Articles


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