A lightweight deep-learning radar gesture recognition based on a structured pruning-NAS
- Title
- A lightweight deep-learning radar gesture recognition based on a structured pruning-NAS
- Author(s)
- Son, Eungang ; Song, Seungeon ; Lee, Jonghun
- Issued Date
- 2023-10-13
- Citation
- International Conference on Information and Communication Technology Convergence, ICTC 2023, pp.1729 - 1731
- Type
- Conference Paper
- ISBN
- 9798350313277
- ISSN
- 2162-1241
- Abstract
- This paper proposes a structured pruning-network architecture search (NAS) algorithm for a lightweight deep-learning radar foot gesture recognition in a conventional lightweight deep-learning models to quantitatively evaluate its performance. Our goal is to recognize foot gestures using a CW radar, generate their STFT unique signatures, and build a foot gesture recognition system that could be implemented on an edge device. The proposed scheme shows that model size and FLOPs were reduced, and a sub-optimal lightweight model for a foot gesture recognition device based on MobileNet was obtained with a slight decrease in accuracy. © 2023 IEEE.
- Publisher
- 한국통신학회 (The Korean Institute of Communications and Information Sciences, KICS)
- Related Researcher
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Song, Seungeon
- Research Interests
-
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