We augment human pose estimation (openpifpaf library) by support for multi-camera and multi-person tracking and a long short-term memory (LSTM) neural network to predict two classes: “Fall” or “No Fall”. From the poses, we extract five temporal and spatial features which are processed by an LSTM classifier.
pip install -r requirements.txtpython3 fall_detector.py| Argument | Description | Default |
|---|---|---|
| num_cams | Number of Cameras/Videos to process | 1 |
| video | Path to the video file (None to capture live video from camera(s)) For single video fall detection(--num_cams=1), save your videos as abc.xyz and set --video=abc.xyz For 2 video fall detection(--num_cams=2), save your videos as abc1.xyz & abc2.xyz & set --video=abc.xyz | None |
| save_output | Save the result in a video file. Output videos are saved in the same directory as input videos with "out" appended at the start of the title | False |
| disable_cuda | To process frames on CPU by disabling CUDA support on GPU | False |
Please cite the following paper in your publications if our work has helped your research:
Multi-camera, multi-person, and real-time fall detection using long short term memory
@inproceedings{Taufeeque2021MulticameraMA,
title={Multi-camera, multi-person, and real-time fall detection using long short term memory},
author={Mohammad Taufeeque and Samad Koita and N. Spicher and T. Deserno},
booktitle={Medical Imaging},
year={2021}
}