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.txt
python3 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,
author = {Mohammad Taufeeque and Samad Koita and Nicolai Spicher and Thomas M. Deserno},
title = {{Multi-camera, multi-person, and real-time fall detection using long short term memory}},
volume = {11601},
booktitle = {Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {35 -- 42},
year = {2021},
doi = {10.1117/12.2580700},
URL = {https://doi.org/10.1117/12.2580700}
}