This repository implements a four-stage end-to-end system for detecting and responding to unauthorized entry:
-
CSI-Based Presence Detection
Continuously monitor WiFi CSI (Channel State Information) to detect door openings or human entry. -
Intruder Localization
Analyze phase and amplitude shifts to approximate the intruder’s position in the room. -
Real-Time Human Detection & Tracking
- YOLOv8 for ultra-fast person bounding-box detection
- SAM2 + SAMURAI for pixel-precise segmentation, centroid extraction, and PTZ camera control
-
Logging & Anomaly Alerts
Record intrusion events, track “stationary interaction” behaviors, and push logs/alerts to the dashboard or mobile.
By combining wireless sensing, computer vision, and intelligent logging, this pipeline delivers robust, automated intrusion monitoring in real time.
SAM2 (Segment Anything Model 2) is designed for object segmentation and tracking but lacks built-in capabilities for performing this in real time.
SAMURAI enhances SAM2 by introducing motion modeling, leveraging temporal motion cues for better tracking accuracy without retraining or fine-tuning.
-
YOLOv8 Person Detection
Ultra-fast bounding-box detection of humans in each video frame. -
SAM2 Segmentation & Tracking
Pixel-accurate masks + centroid extraction to hand off to the PTZ controller. -
Motion-Aware Tracking
SAMURAI motion modeling ensures stable multi-object tracks without retraining. -
Anomaly Alerting
Detects “stop-and-interact” behavior (e.g., a thief grabbing an object) and generates an alert.
I recommend using uv venv to create isolated environments, simplifying dependency management and ensuring reproducible setups.
# Install the 'uv' CLI and create a new venv
pip install uv
uv venv
# On macOS / Linux
source .venv/bin/activate
# On Windows (PowerShell)
source .venv/Scripts/activategit clone https://github.com/NVA-Lab/intrusion-detector.git
cd intrusion-detector
# Install the core package (SAM2 + demo app) in editable mode
uv pip install -e .
### 4. Download SAM2 Checkpoints
```bash
cd checkpoints
./download_ckpts.sh
cd ..
This project leverages:
- YOLOv8 by Ultralytics for ultra-fast real-time person detection.
- SAM2 by Meta FAIR for pixel-precise segmentation and tracking.
- SAMURAI by the University of Washington’s Information Processing Lab for motion-aware memory modeling.
@article{glenn2024yolov8,
title={YOLOv8: Next-Generation Real-Time Object Detection},
author={Glenn Jocher and Ultralytics},
year={2024},
url={https://github.com/ultralytics/ultralytics}
}
@article{ravi2024sam2,
title={SAM 2: Segment Anything in Images and Videos},
author={Ravi et al.},
year={2024},
url={https://arxiv.org/abs/2408.00714}
}
@misc{yang2024samurai,
title={SAMURAI: Adapting SAM for Zero-Shot Visual Tracking with Motion-Aware Memory},
author={Yang et al.},
year={2024},
url={https://arxiv.org/abs/2411.11922}
}