An AI-powered vision system designed to enhance passenger experience and optimize operational efficiency at the YVR airport by utilizing machine learning to track passenger volumes and identify maintenance needs in real-time using security cameras.
Developed by Team Ora for YVR Smart Airport Hackathon 2024. 🏆 Won 1st place in the hackathon. For more information, read the news article here.
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| Login Page | Dashboard Page |
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| Reports Page | Live Monitor Page |
- React.js
v18.2.0 - Node.js
- TailwindCSS
- Python
- Flask
v3.0.3 - Redux
v5.0.1 - YOLOv8
- TensorFlow Lite
- Raspberry Pi
- Bosch Security Camera
- Airport Maintenance Monitoring: Detect and monitor maintenance issues within the airport premises, ensuring timely intervention and upkeep.
- Spill Detection: With its advanced image recognition algorithms, Baaj can identify spills or hazards on the airport floors, enhancing safety protocols.
- Passenger Volume Tracking: Track passenger volumes throughout the airport, providing valuable insights for operational management and resource allocation.
- Ceiling-mounted Camera Integration: Designed to work seamlessly with ceiling-mounted cameras, Baaj offers optimal coverage and perspective for surveillance and analysis.
Caution
Please note that the final version requires an coral edge tpu to run and what is provided here is an equivalent that can be run on most computers, however, it may not be as performant.
Once Baaj is installed and configured, you can use it to monitor airport maintenance, detect spills, and track passenger volumes.