Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/m-esmat/car-accidetnt-detection

This project aims to develop a machine learning-based car accident detection system using video surveillance . The system analyzes real-time or recorded videos to automatically identify potential car accidents or dangerous driving events, providing quick alerts to improve response times in emergency situations.
https://github.com/m-esmat/car-accidetnt-detection

Last synced: about 1 month ago
JSON representation

This project aims to develop a machine learning-based car accident detection system using video surveillance . The system analyzes real-time or recorded videos to automatically identify potential car accidents or dangerous driving events, providing quick alerts to improve response times in emergency situations.

Awesome Lists containing this project

README 

        
# Project Overview:

The Car Accident Detection System is a machine learning-based solution designed to identify and alert for potential car accidents or dangerous driving events in real-time using video surveillance footage. The system processes both live and recorded videos, such as dashcam footage or traffic camera feeds, to automatically detect accidents or risky driving behavior. By leveraging advanced computer vision and deep learning techniques, the system aims to provide timely alerts to emergency services, enabling faster response times and enhancing road safety.



# Objective:

The primary objective of this project is to develop an automated system that can accurately detect car accidents from video footage. The system will:



Detect accidents in real-time or on recorded video.

Provide immediate alerts to emergency responders or relevant authorities.

Analyze video data to identify dangerous driving events, such as collisions, abrupt stops, or erratic behavior.

By improving the speed and efficiency of accident detection, the project seeks to help reduce response times, mitigate road traffic risks, and contribute to overall traffic safety. The system is scalable, allowing it to be deployed in various applications, from traffic monitoring stations to vehicle-mounted cameras.



# Key Features:

Real-time Accident Detection: Capable of analyzing live video streams to detect accidents instantly.

Accurate Alerts: Provides real-time alerts upon detecting an accident, improving emergency response times.

Machine Learning Models: Utilizes advanced models like Convolutional Neural Networks (CNNs) and object detection models (e.g., YOLO) for high-accuracy accident detection.

Scalable Solution: Can be implemented in different environments, such as traffic monitoring systems, vehicles, or urban surveillance setups.



# Technologies Used:

Deep Learning Frameworks: TensorFlow, Keras, pytorch



# Dataset:

The project utilizes a custom dataset consisting of video clips from various sources, including traffic cameras and dashcam footage. The dataset contains labeled instances of both normal driving behavior and car accidents, providing the necessary data for training and evaluating the machine learning model.



Dataset Source: [https://www.kaggle.com/datasets/ckay16/accident-detection-from-cctv-footage], [https://www.kaggle.com/datasets/fahaddalwai/cctvfootagevideo]