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  Real love doesn’t make you wait, and neither does PulseQ

🌀 PulseQ

Real love doesn’t make you wait—neither should your queue.

🚀 Inspiration

Waiting in long queues feels like heartbreak—frustrating, unnecessary, and preventable. We wanted to fix this using the power of AI, just like how real love doesn’t make you wait.

💡 What it does

PulseQ uses camera-based AI to monitor queues in real-time, predict wait times, detect crowd density, and optimize flow—making queue management smart, seamless, and human-centric.

🛠️ How we built it

• YOLO for real-time crowd detection
  
• Python (Flask) for backend API
  
• Node.js and Express.js for routing
  
• HTML, CSS, and JavaScript for frontend
  
• Integrated live camera streams for real-time processing

🚧 Challenges we ran into

• Integrating YOLO with live camera feeds
  
• Synchronizing frontend and backend communication
  
• Managing real-time performance with minimal latency

🏆 Accomplishments that we're proud of

• End-to-end real-time queue tracking system
  
• AI-powered crowd detection working with actual video streams
  
• Seamless frontend and backend integration within the hackathon timeframe

📚 What we learned

• Hands-on with YOLO and OpenCV
  
• Backend API development with Flask and Express
  
• Working with camera inputs and live video analytics in web apps

🔮 What's next for PulseQ

• Predictive queue wait time analysis
  
• SMS/notification-based token system
  
• Public deployment in colleges, hospitals, and banks
  
• Mobile-friendly app version

Built With

• camera
• css
• deepface
• express.js
• html
• javascript
• mongodb
• node.js
• opencv
• python
• yolov8