f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
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System Requirements Specification (SRS)
WiFi-DensePose System
Document Information
- Version: 1.0
- Date: 2025-01-07
- Project: InvisPose - WiFi-Based Dense Human Pose Estimation
- Status: Draft
1. Introduction
1.1 Purpose
This document specifies the system requirements for the WiFi-DensePose system, a revolutionary privacy-preserving human pose estimation platform that transforms commodity WiFi infrastructure into a powerful human sensing system.
1.2 Scope
The system enables real-time full-body tracking through walls using standard mesh routers, achieving 87.2% detection accuracy while maintaining complete privacy preservation without cameras or optical sensors.
1.3 Definitions and Acronyms
- CSI: Channel State Information - WiFi signal characteristics containing amplitude and phase data
- DensePose: Dense human pose estimation mapping 2D detections to 3D body models
- MIMO: Multiple-Input Multiple-Output antenna configuration
- AP@50: Average Precision at 50% Intersection over Union
- FPS: Frames Per Second
- RTMP: Real-Time Messaging Protocol
2. Overall Description
2.1 Product Perspective
The WiFi-DensePose system operates as a standalone platform that integrates with existing WiFi infrastructure to provide human sensing capabilities across multiple domains including healthcare, retail, and security applications.
2.2 Product Functions
- Real-time human pose estimation through WiFi signals
- Multi-person tracking and identification
- Cross-wall detection capabilities
- Domain-specific analytics and monitoring
- Live streaming and visualization
- API-based integration with external systems
2.3 User Classes
- Healthcare Providers: Elderly care monitoring, patient activity tracking
- Retail Operators: Customer analytics, occupancy monitoring
- Security Personnel: Intrusion detection, perimeter monitoring
- Developers: API integration, custom application development
- System Administrators: Deployment, configuration, maintenance
3. Hardware Requirements
3.1 WiFi Router Requirements
3.1.1 Compatible Hardware
- Primary: Atheros-based routers (TP-Link Archer series, Netgear Nighthawk)
- Secondary: Intel 5300 NIC-based systems
- Alternative: ASUS RT-AC68U series
3.1.2 Antenna Configuration
- Minimum: 3×3 MIMO antenna configuration
- Spatial Diversity: Required for CSI spatial measurements
- Frequency Bands: 2.4GHz and 5GHz support
3.1.3 Firmware Requirements
- Base: OpenWRT firmware compatibility
- Patches: CSI extraction patches installed
- Monitor Mode: Capability for monitor mode operation
- Data Streaming: UDP data stream support
3.1.4 Cost Constraints
- Target Cost: ~$30 per router unit
- Total System: Under $100 including processing hardware
- Scalability: 10-100x cost reduction vs. LiDAR alternatives
3.2 Processing Hardware Requirements
3.2.1 Minimum Specifications
- CPU: Multi-core processor (4+ cores recommended)
- RAM: 8GB minimum, 16GB recommended
- Storage: 50GB available space
- Network: Gigabit Ethernet for CSI data streams
3.2.2 GPU Acceleration (Optional)
- CUDA Support: NVIDIA GPU with CUDA capability
- Memory: 4GB+ GPU memory for real-time processing
- Performance: Sub-100ms processing latency target
3.2.3 Network Infrastructure
- Bandwidth: Minimum 100Mbps for CSI data collection
- Latency: Low-latency network for real-time processing
- Reliability: Stable connection for continuous operation
4. Software Requirements
4.1 Operating System Support
- Primary: Linux (Ubuntu 20.04+, CentOS 8+)
- Secondary: Windows 10/11 with WSL2
- Container: Docker support for deployment
4.2 Runtime Dependencies
- Python: 3.8+ with pip package management
- PyTorch: GPU-accelerated deep learning framework
- OpenCV: Computer vision and image processing
- FFmpeg: Video encoding for streaming
- FastAPI: Web framework for API services
4.3 Development Dependencies
- Testing: pytest, unittest framework
- Documentation: Sphinx, markdown support
- Linting: flake8, black code formatting
- Version Control: Git integration
5. Performance Requirements
5.1 Accuracy Metrics
- Primary Target: 87.2% AP@50 under optimal conditions
- Cross-Environment: 51.8% AP@50 minimum performance
- Multi-Person: Support for up to 5 individuals simultaneously
- Tracking Consistency: Minimal ID switching during occlusion
5.2 Real-Time Performance
- Processing Rate: 10-30 FPS depending on hardware
- End-to-End Latency: Under 100ms on GPU systems
- Startup Time: System ready within 30 seconds
- Memory Usage: Stable operation without memory leaks
5.3 Reliability Requirements
- Uptime: 99.5% availability for continuous operation
- Error Recovery: Automatic recovery from transient failures
- Data Integrity: No data loss during normal operation
- Graceful Degradation: Reduced performance under resource constraints
5.4 Scalability Requirements
- Concurrent Users: Support 100+ API clients
- Data Throughput: Handle continuous CSI streams
- Storage Growth: Efficient data management for historical data
- Horizontal Scaling: Support for distributed deployments
6. Security Requirements
6.1 Privacy Protection
- No Visual Data: Complete elimination of camera-based sensing
- Anonymous Tracking: Pose data without identity information
- Data Encryption: Encrypted data transmission and storage
- Access Control: Role-based access to system functions
6.2 Network Security
- Secure Communication: HTTPS/WSS for all external interfaces
- Authentication: API key-based authentication
- Input Validation: Comprehensive input sanitization
- Rate Limiting: Protection against abuse and DoS attacks
6.3 Data Protection
- Local Processing: On-premises data processing capability
- Data Retention: Configurable data retention policies
- Audit Logging: Comprehensive system activity logging
- Compliance: GDPR and healthcare privacy compliance
7. Environmental Requirements
7.1 Physical Environment
- Operating Temperature: 0°C to 40°C
- Humidity: 10% to 90% non-condensing
- Ventilation: Adequate cooling for processing hardware
- Power: Stable power supply with UPS backup recommended
7.2 RF Environment
- Interference: Tolerance to common WiFi interference
- Range: Effective operation within 10-30 meter range
- Obstacles: Through-wall detection capability
- Multi-Path: Robust operation in complex RF environments
7.3 Installation Requirements
- Router Placement: Strategic positioning for coverage
- Network Configuration: Isolated or VLAN-based deployment
- Calibration: Environmental baseline establishment
- Maintenance Access: Physical and remote access for updates
8. Compliance and Standards
8.1 Regulatory Compliance
- FCC Part 15: WiFi equipment certification
- IEEE 802.11: WiFi standard compliance
- IEEE 802.11bf: Future WiFi sensing standard compatibility
- Local Regulations: Regional RF emission compliance
8.2 Industry Standards
- ISO 27001: Information security management
- HIPAA: Healthcare data protection (where applicable)
- GDPR: European data protection regulation
- SOC 2: Service organization control standards
9. Quality Attributes
9.1 Usability
- Installation: Automated setup and configuration
- Interface: Intuitive web-based dashboard
- Documentation: Comprehensive user and API documentation
- Support: Multi-language support for international deployment
9.2 Maintainability
- Modular Design: Component-based architecture
- Logging: Comprehensive system and error logging
- Monitoring: Real-time system health monitoring
- Updates: Rolling updates without service interruption
9.3 Portability
- Cross-Platform: Support for multiple operating systems
- Containerization: Docker-based deployment
- Cloud Compatibility: Support for cloud deployment
- Hardware Independence: Adaptation to different hardware configurations
10. Constraints and Assumptions
10.1 Technical Constraints
- WiFi Dependency: Requires compatible WiFi hardware
- Processing Power: Performance scales with available compute resources
- Network Bandwidth: CSI data requires significant bandwidth
- Environmental Factors: Performance affected by RF environment
10.2 Business Constraints
- Cost Targets: Maintain affordability for widespread adoption
- Time to Market: Rapid deployment capability
- Regulatory Approval: Compliance with local regulations
- Intellectual Property: Respect for existing patents and IP
10.3 Assumptions
- Network Stability: Reliable network infrastructure
- Power Availability: Stable power supply
- User Training: Basic technical competency for deployment
- Maintenance: Regular system maintenance and updates
11. Acceptance Criteria
11.1 Functional Acceptance
- Pose Detection: Successful human pose estimation
- Multi-Person: Concurrent tracking of multiple individuals
- Real-Time: Sub-100ms latency performance
- API Functionality: All specified endpoints operational
11.2 Performance Acceptance
- Accuracy: Meet specified AP@50 targets
- Throughput: Achieve target FPS rates
- Reliability: 99.5% uptime over 30-day period
- Resource Usage: Operate within specified hardware limits
11.3 Integration Acceptance
- External APIs: Successful integration with specified services
- Streaming: Functional Restream integration
- Webhooks: Reliable event notification delivery
- MQTT: Successful IoT ecosystem integration
// TEST: Verify all hardware requirements are met during system setup // TEST: Validate performance metrics under various load conditions // TEST: Confirm security requirements through penetration testing // TEST: Verify compliance with regulatory standards // TEST: Validate acceptance criteria through comprehensive testing