Cited 68 time in
Cited 85 time in
Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks
- Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks
- Kim, Youngwook; Moon, Taesup
- DGIST Authors
- Moon, Taesup
- Issue Date
- IEEE Geoscience and Remote Sensing Letters, 13(1), 8-12
- Article Type
- Activity Classifications; Classification (of Information); Classification Boundary; Convolution; Convolutional Neural Network; Deep Learning; Domain Knowledge; Doppler Radar; High-Accuracy; Human Activities; Human Activity Classification; Human Detection; Learning Algorithms; Micro-Doppler; Neural Networks
- We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification. © 2015 IEEE.
- Institute of Electrical and Electronics Engineers Inc.
There are no files associated with this item.
- Department of Information and Communication EngineeringMachine Learning and Data Science Lab1. Journal Articles
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.