HAAR_DeepModel (v.0.5)

Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives.
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Table of Contents

1. About The Project
   • Requirenments
2. Getting Started
3. Contributing
4. License
5. Acknowledgments

About The Project

Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.

We defined activity of ship-passenger. (normal, abnormal)
[Walking, Lean-railing, Sit-down, Smoking, Move-Over, Standing ... ]

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Requirements

• Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
• Detectron 2
  • Linux or macOS with Python ≥ 3.7
  • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
  • OpenCV is optional but needed by demo and visualization
  • See Detectron Install.md
• AdelaiDet
  • Detectron2 base
  • See AedlaiDet Install.md
  • FCPose, FCOS-Detection, Boxinst

Download pretrain Model : Key-Point

Download pretrain Model : Faster-RCNN

Download pretrain Model : Retinanet

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Getting Started

The model can be started by executing haar_demo.py in /HAAR_Demo directory.

[Sample Run Script] (use custom model)

python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/mymodel.pth

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Acknowledgments

• SSL
• Dr.Hong
• ...

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