https://github.com/taufeeque9/humanfalldetection

Real-time, Multi-person & Multi-camera Fall Detector in Python
https://github.com/taufeeque9/humanfalldetection

deep-learning fall-detection human-pose-estimation long-short-term-memory lstm multiple-cameras neural-networks python3-fall-detector video-processing

Last synced: 7 days ago
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Real-time, Multi-person & Multi-camera Fall Detector in Python

• Host: GitHub
• URL: https://github.com/taufeeque9/humanfalldetection
• Owner: taufeeque9
• Created: 2020-04-15T13:30:40.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-09-29T18:01:51.000Z (about 2 months ago)
• Last Synced: 2024-10-15T17:50:02.463Z (about 1 month ago)
• Topics: deep-learning, fall-detection, human-pose-estimation, long-short-term-memory, lstm, multiple-cameras, neural-networks, python3-fall-detector, video-processing
• Language: Python
• Homepage:
• Size: 512 MB
• Stars: 251
• Watchers: 7
• Forks: 62
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

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README

        
# 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

```shell script

pip install -r requirements.txt

```

## Usage

```shell script

python3 fall_detector.py

```

ArgumentDescriptionDefault

num_cams Number of Cameras/Videos to process1

videoPath 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.xyzNone

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 titleFalse

disable_cuda To process frames on CPU by disabling CUDA support on GPUFalse

## Dataset

We used the [UP-Fall Detection](https://sites.google.com/up.edu.mx/har-up/) to train the LSTM model. You can use [this](https://colab.research.google.com/drive/1PbzVZnwBzFK_CcMf5G3dFrjwKZgfK3Vy?usp=sharing) 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](https://doi.org/10.1117/12.2580700)

                  

    @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}

                  }