Amrita Canteen App

Overview: The Amrita Canteen App is an innovative web application designed to enhance the dining experience by providing real-time insights into canteen operations. Leveraging advanced deep learning (DL) techniques, the app processes video feeds from CCTV cameras to analyze crowd statistics and provide detailed menu insights, helping both students and staff make informed decisions.

Key Features:

  • Real-Time Crowd Monitoring: Uses deep learning models to analyze CCTV footage, providing real-time updates on crowd density and queue lengths, helping users decide the best time to visit the canteen.
  • Menu Insights and Recommendations: Automatically scans and updates the daily menu, offering personalized food recommendations based on user preferences and historical choices.
  • Data-Driven Decision Making: Offers valuable analytics to canteen management for optimizing operations, improving service efficiency, and reducing wait times.

Project Highlights:

  • Advanced Deep Learning Techniques: Utilizes state-of-the-art deep learning algorithms to process and analyze video data efficiently, ensuring high accuracy in crowd estimation and menu digitization.
  • Impactful & Scalable: Designed to be adopted by canteens across the campus, providing a scalable solution that can be customized to different canteen settings and operational needs.
  • Collaborative Development: Developed with inputs from both technical teams and end-users to ensure the application meets practical needs and usability standards.

Technologies Used:

  • Programming Languages: Python, JavaScript
  • Frameworks and Tools: TensorFlow/PyTorch (for deep learning models), OpenCV (for video processing), Django/Flask (for backend development)
  • Data Processing: Advanced computer vision techniques for real-time video analysis

Outcome: The Amrita Canteen App represents a significant step forward in integrating technology with everyday campus life. Currently under review by the college board, the app is poised for deployment in canteens to enhance user experience and streamline canteen operations through smart, data-driven insights.

Project Code: To access the project code and learn more about the development process, visit the repository link.