CrowdVision AI – Real-Time Crowd Monitoring & Intelligence System

An AI-powered computer vision platform that detects and counts people in real time, monitors crowd density and occupancy, tracks entry and exit movements, and provides actionable crowd intelligence through a live monitoring dashboard.

Overview CrowdVision AI is an intelligent crowd monitoring and safety analytics platform designed to help organisations monitor public spaces, understand crowd behaviour, and identify potentially unsafe crowd conditions. The system uses computer vision and machine learning inference to analyse camera feeds and video streams, detect people, estimate occupancy, classify crowd density, and monitor movement across designated zones.

The Problem: Monitoring crowded environments manually can be difficult, time-consuming, and inefficient. Security personnel may struggle to maintain accurate occupancy counts, monitor multiple zones, and identify unusual crowd conditions quickly. CrowdVision AI was developed to explore how computer vision and real-time analytics could improve crowd visibility and monitoring.

My Solution: I developed a computer vision system integrating a pretrained YOLO object-detection model with OpenCV for video processing.

The platform includes: Real-time people detection and counting. Crowd occupancy estimation. Crowd-density classification. Entry and exit line counting. Zone-based monitoring. Incident and anomaly monitoring workflows. Camera health and tamper monitoring. Evidence management and reporting. Telegram alert notifications. Remote edge camera processing. Live monitoring dashboard.

Technical Implementation: Machine Learning & Computer Vision Python YOLO / Ultralytics OpenCV NumPy Pandas

Backend: Python FastAPI REST API

Frontend: React Vite JavaScript Other Technologies Git GitHub Telegram Bot API JSON/CSV data storage

My Role Founder & Full-Stack Machine Learning Developer

For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?

Still checking competitor prices by hand? I built a price tracker, and a real run caught 3 bugs my tests had missed.

What it does:

Paste a product URL: it scrapes the price, stock and rating.

Every scrape is saved, so you get the full price history, not just today's number.

Each new price is compared with the last one, up or down, in %.

Current, lowest, highest and average price on a dashboard and a REST API.

The real run: 7 products scraped live from a public store built for scraping practice. Then three bugs showed up:

Every live page came back empty. The scraper asked for compressed pages it couldn't decode.

That empty result wiped a saved product's title and price. Now an unreadable page is refused, and your data stays safe.

The price chart never appeared. The page's own security policy blocked the chart library, so the app now serves it itself.

Each fix has a test that fails without it. 99 tests in total.

Case study and code: https://contra.com/p/YZLvPrXg-e-commerce-price-tracking-scraper-and-price-history

Which prices do you check by hand every week? Tell me the sites, and I'll tell you how I'd track them.

For the REST API, I'd expose the last successful scrape time alongside the latest attempt's status. Can a caller tell "price unchanged" apart from "refresh failed, showing the previous price"?