SafeFall: AI-Powered Fall Detection & Alert System with Python & Computer Vision.
Welcome to the AI-Powered Fall Detection & Alert System with YOLOv8, MediaPipe, and Flask course!
What you’ll learn
- Understand the fundamentals of fall detection using computer vision and its significance in enhancing elderly care, workplace safety, and real-time monitoring..
- Set up a Python development environment with essential libraries, including OpenCV and MediaPipe, for real-time human pose estimation and fall detection..
- Explore the YOLOv8n model for accurate and efficient person detection in live video streams..
- Utilize MediaPipe to extract human skeletal key points for precise fall detection..
- Learn preprocessing techniques for video frames, including normalization and resizing, to improve model performance and real-time processing efficiency..
- Implement real-time visualization of detection outputs by annotating video frames with bounding boxes, skeletal structures, and fall alerts..
- Address challenges such as occlusions, varying camera angles, and differences in body postures to improve detection accuracy..
- Develop an MQTT-based real-time alert system that notifies caregivers or emergency responders when a fall is detected..
- Integrate a SQL database for storing user details, system logs, and incident reports for data analysis and tracking..
- Deploy the system using Flask for backend operations, ensuring smooth data flow and API-based communication with mobile or web applications..
Course Content
- Introduction to AI-Powered Fall Down Detection & Alert System –> 1 lecture • 1min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Fall Down Detection System Project Overview –> 1 lecture • 2min.
- Dependency & Package Overview –> 1 lecture • 3min.
- Installation & MQTT Setup –> 1 lecture • 3min.
- User Registration & Login API –> 1 lecture • 2min.
- MQTT & Flask Integration –> 1 lecture • 2min.
- Fall Detection Logic –> 1 lecture • 2min.
- Prediction API Workflow –> 1 lecture • 8min.
- Code Execution & Testing –> 1 lecture • 5min.
- Wrapping Up –> 1 lecture • 1min.
Requirements
Welcome to the AI-Powered Fall Detection & Alert System with YOLOv8, MediaPipe, and Flask course!
• In this hands-on course, you’ll learn how to build a real-time fall detection system using YOLOv8 for person detection, MediaPipe for skeleton analysis, and Flask for backend processing. This system is designed for elderly care, workplace safety, and real-time emergency monitoring, providing accurate fall detection and instant alerts.
• This course focuses on leveraging YOLOv8 for detecting individuals and MediaPipe for analyzing skeletal movement, ensuring accurate fall detection based on shoulder and leg angle calculations. By the end of the course, you’ll have developed a fully functional real-time fall detection and alert system that integrates Flask, MQTT-based notifications, and SQL for user management.
What You’ll Learn:
• Set up your Python development environment and install essential libraries like OpenCV, MediaPipe, Flask, and MQTT for seamless integration.
• Use the YOLOv8 model to detect human presence and track movements in live video feeds.
• Leverage MediaPipe for extracting skeletal points and calculating shoulder and leg angles to determine falls.
• Preprocess video streams to enhance detection performance, handling variations in lighting, camera angles, and occlusions.
• Implement a real-time visualization system, displaying detected falls with bounding boxes and alerts.
• Develop an MQTT-based notification system to instantly alert caregivers, security personnel, or emergency responders when a fall occurs.
• Integrate a SQL database to store user details, incident logs, and system alerts for better monitoring and analysis.
• Deploy the system using Flask, ensuring smooth real-time data processing and API communication with a mobile or web-based dashboard.
• Optimize the system for real-time performance, handling multiple video streams efficiently.
Enroll today and start building your SafeFall: AI-Powered Fall Detection & Alert System