Based on 2024-2025 benchmarks and edge computing research, here are the top recommendations for your specific setup (Raspberry Pi 4 + Intel NCS2).
Why it wins:
- State-of-the-Art (SOTA): Released recently, it offers a better speed/accuracy trade-off than YOLOv8.
- Efficiency: ~30% faster on CPUs than YOLOv8n while maintaining higher accuracy.
- NCS2 Support: Works excellently with OpenVINO FP16 export.
Why consider it:
- Stability: Extremely mature ecosystem and documentation.
- Performance: Very close to v11. If you encounter any "bleeding edge" bugs with v11, v8n is the rock-solid fallback.
- Speed: ~2-5 FPS on bare Pi 4 CPU, ~10+ FPS with NCS2/OpenVINO.
Why consider it:
- Raw Speed: The fastest architecture for older CPUs.
- Trade-off: Significantly less accurate than YOLO. It might miss people in difficult lighting or occlusions.
- Use Case: If you need 30+ FPS and only care about rough estimates, not precise tracking.
- Why: These are designed for thousands of people (e.g., stadiums) where individuals are just pixels.
- Your Use Case: For a hallway/room, identifying individual people (detection) is far more accurate and provides better metrics (IN/OUT counting, tracking).
Since you have an Intel NCS2, the YOLO family (v8n or v11n) exported to OpenVINO is the definitive best choice. It gives you the accuracy of modern deep learning with the speed required for real-time monitoring.