HumanFallDetection

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

Dataset

We used the UP-Fall Detection to train the LSTM model. You can use this Colab notebook to download the download the dataset and compile the files into videos.

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,
                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}
              }