ALICE-3 RICH FEE

Seeing in the Dark: Reconstructing 3D Human Pose Using NIR Single-Pixel Imaging

Estimating a person’s 3D pose and body shape from a single image is a fundamental challenge in computer vision—especially when lighting is poor or the subject is partially occluded. Most traditional approaches rely on RGB images, which often fail in real-world scenarios such as nighttime environments or disaster zones. Our recent work introduces a breakthrough using Single-Pixel Imaging (SPI) in the Near-Infrared (NIR) spectrum (850–1550 nm), combined with Time-of-Flight (TOF) technology. This setup offers a powerful alternative to standard imaging methods. NIR light has the unique ability to penetrate clothing and adapt to changing illumination, making it ideal for human detection in low-visibility conditions. Instead of relying on high-resolution sensors, our SPI system reconstructs 3D point clouds from a series of single-pixel measurements. These point clouds are then processed using advanced deep learning models:

• A Vision Transformer (ViT) aligns the reconstructed human poses with a predefined SMPL-X skeleton model.

• A self-supervised PointNet++ network estimates fine-grained attributes such as global rotation, translation, body shape, and pose.

The image illustrates a comprehensive method for constructing a 3D human model

Our lab experiments simulating night-time environments demonstrate the potential of this system for real-world applications, especially in rescue missions where vision-based solutions often fail. With no dependence on ambient light and an architecture tailored for low-SWaP (size, weight, and power) devices, NIR-SPI could become a core technology for search-and-rescue UAVs, surveillance, or night-time human monitoring.

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Osorio Quero, C.; Durini, D.; Martinez-Carranza, J. ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging. Appl. Sci. 2025, 15, 6138. https://doi.org/10.3390/app15116138

Emergency Response with FPGA-Integrated RF Signal Detection

In disaster-stricken areas, locating victims swiftly is of utmost importance. One of the most effective ways to achieve this is by detecting radio frequency (RF) signals emitted from communication devices. These signals, originating from cellular networks, radio broadcasts, and satellite communications, provide crucial indicators of human presence. However, scanning across multiple frequencies to identify relevant signals efficiently remains a challenge.

https://video.wixstatic.com/video/300297_3652289d53014662b31bb69b5d3c2108/720p/mp4/file.mp4

System Overview

Our system is built around three core components:

1. RTL-SDR Hardware: Provides a flexible and affordable means to scan RF signals over a wide range of frequencies.

2. FPGA-Based Processing: Accelerates real-time signal processing, ensuring fast and efficient classification of detected signals.

3. Deep Neural Networks (DNNs): Three different architectures were implemented and tested to enhance the accuracy of signal classification.

   
   

This integration allows real-time detection of crucial signals in a disaster area, aiding rescue teams in pinpointing survivors and optimizing their response strategies.

Deep Learning Models and FPGA Integration

To maximize the system’s accuracy, we implemented three deep neural network architectures, trained on a diverse dataset of radio modulations. The networks were optimized for FPGA-based acceleration, leveraging DPU cores for real-time inference. The system is capable of recognizing:

• AM-SSB-WC (Amplitude Modulation - Single Side Band)

• AM-DSB-SC (Double Side Band Suppressed Carrier)

• FM (Frequency Modulation)

• QPSK (Quadrature Phase Shift Keying)

• GMSK (Gaussian Minimum Shift Keying)

• 16QAM (16-Quadrature Amplitude Modulation)

• OQPSK (Offset Quadrature Phase Shift Keying)

• 8PSK (8-Phase Shift Keying)

• BPSK (Binary Phase Shift Keying)

• OOK (On-Off Keying)

  
  

Through extensive testing, the best-performing model achieved an impressive classification accuracy of up to 98%. This level of precision significantly enhances the reliability of RF-based emergency detection systems, ensuring that important distress signals are not overlooked.

Future Applications and UAV Integration

Given its high accuracy and real-time processing capabilities, our system presents a strong candidate for UAV-based emergency response operations. Unmanned Aerial Vehicles (UAVs) equipped with this technology can autonomously scan large disaster zones, detecting and locating RF signals from survivors’ communication devices. This approach could drastically reduce the time required to identify individuals in need of assistance.

Conclusion

Our research demonstrates that cost-effective, FPGA-integrated RF signal detection is a viable solution for emergency response applications. By combining RTL-SDR hardware, FPGA-based processing, and deep learning, we have developed a system that achieves high accuracy in detecting crucial radio signals. The promising results open avenues for further development, particularly in UAV-based implementations, ensuring rapid and efficient victim detection in future disaster scenarios. With ongoing advancements in AI and hardware acceleration, this technology has the potential to revolutionize search and rescue operations, making emergency responses more efficient and saving more lives.