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.
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Clone the project:
git clone https://github.com/Thirumurugan-12/nutshell-accident.git
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Go to the project directory and Open Project:
cd nutshell-accident code .
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Install Python Dependencies:
pip install "Python-Dependency-Name"
pip install pandas numpy streamlit joblib IPython opencv-python Run you Application by Executing.
streamlit run app.pygo to LocalHost to View your Live app.
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.
Contributions are welcome! Feel free to submit issues and pull requests.
