I want a desktop-based vision solution that watches any TV, computer monitor, or public display in real time and notifies me the moment someone steps in front of it and tries to take a photo. The core may rely on YOLO or straight OpenCV or Any—whichever gives the fastest, most reliable detection. How it should work • A camera connected to my desktop continuously analyses the scene. • When a person holding up a phone (or clearly preparing to photograph) is detected, the system must: – Crop the frame (or short clip) so the person is centred. – Timestamp it. – Dispatch the alert simultaneously to Telegram, WhatsApp, and Email or Any platform. Key expectations • Sub-second latency between detection and alert. • Clean, well-commented source code (Python preferred) plus a requirements.txt and set-up guide so I can recreate the environment. • Simple JSON or YAML config file to let me adjust confidence thresholds, camera source, and messaging credentials without editing code. • Tested across the three display types listed above; false positives should be minimised. • A quick demo video or live screen share proving the system works end-to-end. Acceptance criteria 1. Running the main script starts live detection with visible bounding boxes. 2. Holding a phone up in front of a TV, monitor, or large public-style display triggers an alert in all three channels within one second. 3. The alert contains the cropped image and readable date/time stamp. 4. Exiting the script cleanly releases the camera and shuts all processes. If you already have pretrained YOLO weights for “person-with-phone” or can fine-tune quickly, even better; otherwise, propose how you’ll gather or augment data. I’m ready to test as soon as you have an MVP, then iterate together for robustness.