jackweiwang / HumanFallDetection

Real-time, Multi-person & Multi-camera Fall Detector in Python

HumanFallDetection

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.

Setup

pip install -r requirements.txt

Usage

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

Citation

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}
              }