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RadarFormer: End-to-End Human Perception With Through-Wall Radar and Transformers
IEEE Transactions on Neural Networks and Learning Systems
|September 22, 2023
Summary
RadarFormer, a novel method using self-attention (SA) on radar echoes, enhances human perception tasks like pose estimation. It outperforms existing radar methods in performance and efficiency, even in challenging environments.
Area of Science:
- Computer Vision
- Signal Processing
- Artificial Intelligence
Background:
- Radar sensors offer advantages over optical cameras for human perception in challenging environments like low-visibility and privacy-sensitive settings.
- Current radar-based human perception often relies on transforming echoes into images for feature extraction using convolutional neural networks.
- These imaging steps can be computationally intensive and may not fully leverage the inherent characteristics of radar signals.
Purpose of the Study:
- To introduce RadarFormer, the first method to apply the self-attention (SA) mechanism directly to radar echoes for human perception tasks.
- To demonstrate the efficacy of processing radar signals end-to-end, bypassing traditional imaging algorithms.
- To establish a new state-of-the-art in radar-based human perception with improved performance and computational efficiency.
Main Methods:
- Developed RadarFormer, a Transformer-like model utilizing fast-/slow-time self-attention (SA) mechanisms tailored for radar signal characteristics.
- Provided theoretical proof that SA processing of radar echoes is at least as expressive as convolutional processing of radar images.
- Enabled end-to-end signal processing directly from radar echoes, eliminating the need for intermediate imaging steps.
Main Results:
- RadarFormer achieved superior performance compared to existing state-of-the-art radar-based methods for human perception tasks.
- The proposed method demonstrated significant improvements in computational cost.
- Accurate human perception results were obtained even in challenging conditions, including darkness and occlusive environments.
Conclusions:
- RadarFormer represents a significant advancement in radar-based human perception by leveraging self-attention directly on echo signals.
- The end-to-end approach offers a more efficient and effective alternative to traditional image-based radar processing.
- The method shows great promise for reliable human sensing in diverse and difficult environmental conditions.
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