DCNN Based Human Activity Recognition Using Micro-Doppler Signatures
Author:
Affiliation:
1. School of Computer Engg. & Mathematical Sciences, DIAT,Pune
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10037242/10037259/10037310.pdf?arnumber=10037310
Reference19 articles.
1. Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine
2. Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity
3. Vision-based human activity recognition: a survey
4. DIAT-μSAT: Small Aerial Targets’ Micro-Doppler Signatures and Their Classification Using CNN
5. Deep Neural Networks for Sensor-Based Human Activity Recognition Using Selective Kernel Convolution
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3. A lightweight hybrid vision transformer network for radar-based human activity recognition;Scientific Reports;2023-10-21
4. Federated Learning and Genetic Mutation for Multi-Resident Activity Recognition;2023 IEEE 19th International Conference on e-Science (e-Science);2023-10-09
5. Through-Wall Human Activity Recognition With Complex-Valued Range–Time–Doppler Feature and Region-Vectorization ConvGRU;IEEE Transactions on Geoscience and Remote Sensing;2023
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