RF Signal Multi-Person Pose Estimation via Cross-Modal Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional RF signal-based multi-person pose estimation methods face limitations due to hardware performance, image quality issues, and the need for separate devices like radar and strategically arranged antennas, especially in environments with obstacles or poor lighting, and require complex preprocessing steps.
Innovation Solution
A processor-implemented method that identifies signal feature maps from RF signals and visual clues through cross-modal supervised learning, integrating information from image-based and RF signal-based Multi-Person Pose Estimation to detect and estimate poses of individuals without relying on high-quality images, using a teacher network to train a student network for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional RF signal-based multi-person pose estimation is used, then it can operate in environments with obstacles or poor lighting, but it requires separate hardware devices like radar and strategically arranged antennas, increasing device complexity
Solution Approach 1:
The patent combines RF signal processing with visual clue extraction within a unified neural network architecture. The RF signal branch and image branch are merged into a multi-branch network that processes both modalities simultaneously, eliminating the need for separate radar hardware and strategic antenna arrangements while maintaining operability in challenging environments.
Solution Approach 2:
The neural network architecture is designed to handle multiple functions: RF signal processing, image processing, visual clue learning, and pose estimation all within a single system. This multi-functional approach replaces the need for dedicated separate hardware devices while maintaining the ability to operate in environments with obstacles or poor lighting.
2Reliability
If conventional RF signal-based multi-person pose estimation is used, then it can detect persons through obstacles, but it requires complex preprocessing steps such as ROI cropping, NMS, and keypoint grouping
Solution Approach 1:
The neural network performs self-service by automatically extracting visual clues from RF signals and performing pose estimation without requiring external preprocessing steps. The network internally handles what would traditionally require separate preprocessing modules, eliminating the need for ROI cropping, NMS, and keypoint grouping while maintaining reliable detection through obstacles.
Solution Approach 2:
The patent extracts visual clues directly from RF signals within the neural network architecture itself, removing the need for separate preprocessing steps. The network extracts and processes the necessary information internally, eliminating the complex preprocessing pipeline while maintaining the ability to detect persons through obstacles.
3Measurement precision
If image-based MPPE learning is used to improve accuracy, then pose estimation performance improves with high-quality images, but it becomes vulnerable to performance degradation when visibility is not guaranteed or image data is limited
Solution Approach 1:
The patent uses a composite approach by combining RF signal processing with image processing in a multi-branch neural network. This composite architecture leverages the strengths of both modalities: high-quality images provide accurate pose estimation when available, while RF signals provide robust detection when visibility is poor, creating a system that maintains performance across varying visibility conditions.
Solution Approach 2:
The neural network acts as an intermediary that learns visual clues from RF signals to supplement or replace image-based information when visibility is poor. This intermediary learning mechanism allows the system to maintain accurate pose estimation by transferring knowledge between modalities, ensuring robust performance regardless of visibility conditions.
Data Source
AI summary
A processor-implemented method including identifying, from received Radio Frequency (RF) signals, a signal feature map, the signal feature map containing information calculated to detect a presence of a person, identifying, from the received RF signals, visual clues according to association information between first information based on a result of image-based Multi-Person Pose Estimation (MPPE) learning and second information based on a result of RF signal-based Multi-Person Pose Estimation (MPPE) learning, detecting one or more persons by utilizing the signal feature map and the visual clues, and detecting one or more poses of the one more persons based on the signal feature map and the visual clues.