Ai-Based Vehicle Accident Detection Using Python And Opencv

[mitsumi]

Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 679.89 MB | Duration: 0h 52m

AI-Powered Accident Detection and Real-Time Monitoring & Alert with Python & Computer Vision


What you'll learn
Understand the fundamentals of accident detection using deep learning models and its significance in enhancing road safety and emergency response systems.
Set up a Python environment using libraries like OpenCV and TensorFlow for data preprocessing, model training, and real-time inference.
Explore Convolutional Neural Networks (CNNs) for image normalization and resizing to improve model accuracy in accident detection.
Implement validation techniques, including classification metrics, recall, and precision, to ensure the reliability of the accident detection model.
Develop a real-time inference pipeline in VS Code to process video feeds and detect accidents efficiently.
Integrate the MQTT protocol for seamless data transmission between edge devices, servers, and mobile applications for instant accident alerts
Build a mobile application using React for the frontend and Flask for the backend to enable live video display and real-time monitoring
Implement real-time alert systems that notify relevant authorities and emergency responders upon detecting an accident.
Address challenges such as false positives, low-light conditions, and varying accident scenarios to improve detection accuracy.
Deploy the system for practical applications in smart cities, traffic monitoring, and intelligent transportation systems to enhance road safety.
Requirements
Basic understanding of Python programming (helpful but not mandatory).
A laptop or desktop computer with internet access[Windows OS with Minimum 4GB of RAM).
No prior knowledge of AI or Machine Learning is required-this course is beginner-friendly.
Enthusiasm to learn and build practical projects using AI and IoT tools.
Description
Welcome to the AI-Powered Accident Detection and Real-Time Monitoring System with Python & Computer Vision! In this hands-on course, you'll learn how to develop a real-time accident detection and alert system using deep learning, IoT, and mobile application technologies. This course covers everything from data preprocessing and model validation to real-time inference, communication protocols, and mobile app integration.What You'll Learn:• Set up your AI development environment with Google Colab, TensorFlow, OpenCV, and essential deep-learning libraries.• Preprocess accident detection data by applying image normalization and resizing using Convolutional Neural Networks (CNNs) for improved accuracy.• Train and validate your accident detection model with classification metrics, recall, and precision to ensure reliability.• Implement real-time inference in VS Code to detect accidents in live video feeds.• Use MQTT protocol for seamless communication between IoT devices, servers, and mobile applications for instant alert notifications.• Build a mobile application with React (frontend) and Flask (backend) to display live video streams and detection results.• Handle real-world challenges such as false positives, low-light conditions, and various accident scenarios to enhance detection accuracy.• Deploy the system for smart city applications, traffic monitoring, and intelligent transportation systems to improve road safety.Enroll today and start building your AI-Powered Accident Detection and Real-Time Monitoring & Alert
Overview
Section 1: Intro to AI Accident Detection & Real-Time Monitoring System
Lecture 1 Introduction
Section 2: Environment Setup for Python Development
Lecture 2 Installing Python
Lecture 3 VS Code Setup for Python Development
Section 3: AI Accident Detection & Real-Time Monitoring System Overview
Lecture 4 AI Accident Detection & Real-Time Monitoring System Overview
Section 4: Configuring the environment on Google Colab
Lecture 5 Google Colab open and google drive mount
Section 5: Setting Up and Exploring Essential Packages
Lecture 6 Packages Information
Section 6: Dataset Acquisition: Downloading & Understanding
Lecture 7 Dataset Acquisition: Downloading & Understanding
Section 7: Dataset Visualization & Analysis
Lecture 8 Dataset Visualization
Section 8: Dataset Preprocessing: Normalization & Resizing
Lecture 9 Dataset Preprocessing: Normalization & Resizing
Section 9: Label Encoding & Data Preparation
Lecture 10 Label Encoding & Data Preparation
Section 10: Training & Validation Data Visualization
Lecture 11 Training & Validation Data Visualization
Section 11: CNN Model Implementation & Training
Lecture 12 CNN Model Implementation & Training
Section 12: Downloading and Saving Trained Model Weights
Lecture 13 Downloading and Saving Trained Model Weights
Section 13: Understanding MQTT Protocol & Package Requirements
Lecture 14 Understanding MQTT Protocol & Package Requirements
Section 14: Model Inference Code Walkthrough
Lecture 15 Model Inference Code Walkthrough
Section 15: Final Code Execution & Live Demonstration
Lecture 16 Final Code Execution & Live Demonstration
Section 16: Wrapping Up
Lecture 17 Course Wrap-Up
Developers, AI enthusiasts, and students interested in deep learning and real-time monitoring applications.,IoT and embedded systems engineers looking to integrate AI-driven accident detection into real-world systems.,App developers who want to build AI-powered mobile applications with live data visualization.

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