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SafeJourney-AI is an advanced driver drowsiness detection and road safety enhancement system. Powered by machine learning and deep learning, it analyzes in-car camera feeds in real-time to ensure driver alertness and enhance road safety.

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SafeJourney-AI

SafeJourney-AI is an advanced driver drowsiness detection and road safety enhancement system. Powered by machine learning and deep learning, it analyzes in-car camera feeds in real-time to ensure driver alertness and enhance road safety.

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Install requirements.txt

pip install requirements.txt

Required Python Version

python == 3.12

Additional requirements

This Project depends on dlib which can be downloaded and installed seperately. Download dlib Install the downloaded file using : pip install dlib-19.24.99-cp312-cp312-win_amd64.whl [ Note run the pip command on the folder where the file got downloaded ]

Running the Model

run the model by running Graph.py which is located inside the *server/*dir. [ Relative path : server/Graph.py]

Seeing the Results

After Running Graph.py Navigate to http://127.0.0.1:8050/ in any browser of you choice to see the live graph and the facial landmarks.

Debugging and limitations

If found any bugs and if you face any issues while setting up the repo. Raise a issue. This project was tested on python version Python 3.12 Browser Used : Firefox Version 118.0.1 platform : Windows 11 Version 22H2 ( OS Build 22624.1616 )

If you experience issues related to the model's predictions, it might be due to the poor quality of the laptop camera or webcam, as they often have less than 1MP and lack low light capturing capabilities. These cameras are suitable for video conferences but may limit the model's performance in image classification tasks. Additionally, this model is specifically designed to work with cameras like product, which offer better resolution (1080p) and have 5MP with dedicated night mode.

Sample Result

Output Screen 1 Output Screen 2

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SafeJourney-AI is an advanced driver drowsiness detection and road safety enhancement system. Powered by machine learning and deep learning, it analyzes in-car camera feeds in real-time to ensure driver alertness and enhance road safety.

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  • Python 73.1%
  • C 26.2%
  • HTML 0.7%