Down below are all the steps that you need to follow in the exact same order as mentioned to run this project on your local machine
- Fork this repository using
- Run this command to clone the repository
git clone https://github.com/47-dev/Anti-Accident-Detection-Model - Create a new virtual environment using the command
conda activate venv/ - Activate the environment using the command
env\Scripts\activate.bat - Insall and import all the libraries
The aim of this project is to develop a Deep Learning-based system that can detect approaching fatal crashes by continually monitoring the head, face, and eye movements of the driver. The system will be able to detect signs of driver distraction, lack of focus, and other unsafe driving behaviours that can result in collisions. We seek to develop an AI system that incorporates Deep Learning Algorithms that automatically identify, avoid, and warn on any likelihood of mishaps using the most feasible and efficient way possible to address the current difficulties of mishaps, especially in mountainous locations.
To record the driver's behavior, the initiative will combine video processing methods, neural networks, and Deep Learning. To use a dashboard-mounted camera, face face recognition, and a gyroscopic sensor, the system will monitor the driver's head gestures, eye movements, and facial expressions. A collection of driving scenarios will be used to train the machine learning algorithm on various driving patterns of behavior that may cause an accident. The system will then responsibility practices data to determine whether the motorist is engaging in any risky conduct and notify him or her to stop.
The objective of this endeavor is to create a trustworthy anti-vehicle accident warning system that can aid in preventing collisions brought on by driver inattention, sleepiness, as well as other risky behaviours. The technology will contribute to increased traffic safety as well as a decrease in incidents.
This technology has a big potential effect since it might be able to stop mishaps brought on by distracted driving, drowsy driving, and other risky habits. Also, it will make driving better for every person by lowering the amount of fatalities as well as injuries on the roadways. Automobile makers, transportation providers, and citizens who want to increase traffic safety can all benefit from the effort.
Here is the link that you can use for accesing the code file 👉 https://github.com/47-dev/Anti-Accident-Detection-Model/blob/main/main_Code.ipynb
Link to access 👉 https://link-orpin.vercel.app/ Link to access 👉 https://link-47-dev.vercel.app/
Competitors had the thrilling opportunity to demonstrate off their talents and ingenuity by creating original answers to real-world issues during the Frosthack hack. We believe that the directions as well as code snippets in the this quickstart guide file will benefit contributors in their initiatives. The demonstration video is an excellent tool for showcasing the functionality and functioning of the developing and emerging during in the hacking.