NutShell

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A novel system for predicting traffic accidents using 3D vehicle tracking. The system uses 3D models to accurately track vehicles and a Convolutional Neural Network (CNN) to learn unique activity patterns based on vehicle trajectories and velocities.
A probability model is then developed to predict the likelihood of traffic accidents. This data-driven approach can improve safety by identifying high-risk areas and driver behaviour, optimizing traffic flow, and contributing to a safer and more efficient transportation system.

Tech Stack

Architecture Design

Snapshots of Final Product

Run Locally

Prerequisites

• Install git: git
• Install VSCode: VSCODE
• Install nodejs: Nodejs

Clone the project:

     git clone https://github.com/NutShell-Police/nutshell-front-end.git

Go to the project directory and Open Project:

      cd nutshell-front-end
      code .

Resolving Python Dependencies

      npm install

Run you Application

Run you Application by Executing.

     npm run dev

go to LocalHost to View your Live app.

What positive and unique solutions your idea have?

Advanced Vehicle Tracking and Traffic Flow Optimization

• Utilizes advanced 3D vehicle tracking technology for real-time vehicle movement monitoring.
• Uses Convolutional Neural Networks (CNNs) to learn unique activity patterns from vehicle trajectories and velocities.
• Detects subtle changes in driver behaviour to detect potential risks.
• Uses probability modelling for accident prediction, analyzing historical data and current traffic conditions.
• Offers a data-driven approach to optimize traffic flow, identifying congestion hotspots and suggesting alternative routes.
• Aims to contribute to safer and more efficient transportation systems by combining computer vision, machine learning, and probability modelling.

BenchMarking

Contributing

Contributions are welcome! Feel free to submit issues and pull requests.

Authors