CCTV People Counting Analytics

I need a reliable, camera-based solution that can automatically detect and count people captured by our existing CCTV network, regardless of whether the footage comes from indoor halls or outdoor entrances. The end goal is clear: turn those raw counts into actionable customer-analytics data I can slice by time, location, and traffic trend.

Here’s what I’m expecting:
• Accurate real-time or near-real-time counts from standard CCTV streams (no extra infrared hardware).
• Robust performance under varying light and weather because some of our cameras sit outdoors.
• An easy way for me to view and export daily, weekly, and monthly traffic reports—CSV download and a lightweight dashboard are perfect.
• A straightforward deployment path: containerised code (Docker) or a documented install script so my tech team can maintain it.

Ideally you’ll lean on proven computer-vision tools—OpenCV, YOLOv8, TensorFlow, or a comparable model—but I’m open to your preferred stack as long as accuracy and speed hold up. If you have past benchmarks or demos, I’d love to see them.

Acceptance criteria
1. ±3 % counting accuracy in daytime indoor tests, ±7 % for challenging outdoor scenes.
2. Dashboard updates automatically without manual refresh.
3. Clear hand-off: source code, model weights, and a brief technical guide.

Let me know your proposed approach, timeline, and any clarifying questions so we can get started.