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Human Detection Based on Time-Varying Signature on Range-Doppler Diagram Using Deep Neural Networks
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- Authors
- Youngwook Kim, Ibrahim Alnujaim, Sungjin You, Byung Jang Jeong
- Issue Date
- 2021-03
- Citation
- IEEE Geoscience and Remote Sensing Letters, v.18, no.3, pp.426-430
- ISSN
- 1545-598X
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- We propose the detection of humans using millimeter-wave FMCW radar based on time-varying signatures of range-Doppler diagrams using deep recurrent neural networks (DRNNs). Demand for human detection is increasing for security, surveillance, and search and rescue purposes, recently, with a particular focus on urban areas filled with clutter and moving targets. We suggest the classification of targets based on their signatures in range-Doppler plots with time because the signatures can be consecutively observed. We measure five target types: humans, cars, cyclists, dogs, and road clutter using millimeter-wave FMCW radar that transmits fast chirps at 77 GHz. To maximize the classification accuracy using the time-varying range-Doppler signatures of the targets, we investigate and compare the performance of 2-D-deep convolutional neural networks (DCNN), 3-D-DCNN, and DRNN along with 2-D-DCNN. The DRNN plus 2-D-DCNN showed the best performance, and the classification accuracy yields 99%, with the human classification rate of 100%.
- KSP Keywords
- 77 GHz, Best performance, Classification rate, Convolution neural network(CNN), Deep Recurrent Neural Networks, Deep convolutional neural networks, Deep neural network(DNN), FMCW Radar, Human Detection, Human classification, Moving Target