Summary of Trgr: Transmissive Ris-aided Gait Recognition Through Walls, by Yunlong Huang et al.
TRGR: Transmissive RIS-aided Gait Recognition Through Walls
by Yunlong Huang, Junshuo Liu, Jianan Zhang, Tiebin Mi, Xin Shi, Robert Caiming Qiu
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TRGR system is a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system that can recognize human identities through walls using only magnitude measurements of channel state information (CSI) from a pair of transceivers. Leveraging transmissive RIS and a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. A residual convolution network (RCNN) is used as the backbone network to learn robust human information. Experimental results show that TRGR achieves an average accuracy of 97.88% in identifying persons when signals traverse concrete walls. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRGR is a new way to recognize people by their walking style using radio waves. Currently, systems need a direct view and struggle with poor signal quality when signals pass through thick walls. The new system uses special surfaces that can change how they reflect radio waves to improve the signal quality and make it possible to recognize people even behind walls. This is achieved by combining two techniques: reconfigurable intelligent surfaces (RIS) and a special type of neural network called residual convolutional neural networks (RCNN). The results show that this new system can accurately identify people with an accuracy rate of 97.88%. |
Keywords
» Artificial intelligence » Neural network » Optimization » Rcnn
