AI-Driven Video Analytics for Smart Surveillance

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About Course

This project is structured to help you learn how to develop a smart surveillance system that leverages object detection models (YOLO), integrated with a simple user interface built with Streamlit. The goal is to teach core skills and provide hands-on experience in computer vision, deep learning, and cloud deployment using AWS.

 

This is a hands-on project and not a course on computer vision and deep learning. For understanding fundamental concepts in computer vision, we provide various external links within the project that you can check out during the timeline of the project. We provide brief explanation of the code given but we encourage the students to read more about OpenCV, YOLO and AWS from official documents.

Project Overview:

  • Duration: 10 weeks
  • Tech Stack: Python (OpenCV, YOLO, TensorFlow), Streamlit, AWS (SageMaker, Lambda, S3), Docker
  • Objective: Build a smart video surveillance system that can detect objects, identify anomalies, and send real-time alerts for potential security threats. The system will leverage object detection models (YOLO) and deploy it in AWS.
  • Skills Acquired: Computer vision (YOLO, OpenCV), real-time video processing, deep learning, cloud-based AI services.
  • Difficulty: Intermediate to Expert
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What Will You Learn?

  • Computer Vision Techniques: Gain hands-on experience with key computer vision libraries like OpenCV and YOLO for real-time object detection and tracking in video feeds.
  • Deep Learning Fundamentals: Understand the principles of deep learning and how to implement and train models using TensorFlow, enhancing their ability to work with neural networks.
  • Real-Time Data Processing: Learn to process video data in real-time, including techniques for capturing, analyzing, and responding to video streams effectively.
  • Cloud Integration: Acquire skills in deploying models using AWS services like SageMaker and S3, enabling them to work with cloud-based AI solutions and understand how to leverage cloud resources.
  • Alert Systems: Implement real-time alerting mechanisms via email and SMS, developing an understanding of how to notify users about detected anomalies and enhance the system's responsiveness.
  • User Interface Development: Build a user-friendly interface using Streamlit, allowing them to showcase their work and interact with the video analytics system effectively.

Course Content

Week 1-2: Environment Setup and Computer Vision Basics

  • Development Environment Setup
  • Basic Image and Video Processing with OpenCV
  • Quiz 1

Week 3-4: Object Detection using YOLO

Week 5-6: Creating the User Interface with Streamlit

Week 7-8: Deploying the Model on AWS

Week 9-10: Anomaly Detection and Real-Time Alerts

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