falldetection_openpifpaf

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基于 OpenPifPaf 人体姿态估计模型做跌倒检测，结合目标跟踪分析视频中的人体关键点，判断是否发生摔倒，可在 CPU 或 GPU 上运行。

Fall Detection using OpenPifPaf's Human Pose Estimation model

暂无版本435star107fork同步于 2026-11-07 07:32 UTC

跌倒检测人体姿态估计目标跟踪视频分析 OpenPifPaf

对比 GitHub 官网 composite-fields computer-vision cpu deep-learning detection

怎么上手

由 README 提炼，执行前请确认

原文

conda create --name falldetection_openpifpaf python=3.7.6
conda activate falldetection_openpifpaf
git clone https://github.com/cwlroda/falldetection_openpifpaf.git
pip3 install openpifpaf
pip3 install -r requirements.txt

硬件要求按 README 说明

README 未明确列出显存要求；可在 GPU 和 CPU 上运行，示例在单张 NVIDIA Quadro P1000 上运行，测试使用两张 NVIDIA Quadro GV100。

需要 CUDA

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

基于 OpenPifPaf 人体姿态估计的跌倒检测工具，支持多人场景

基于 OpenPifPaf 人体姿态估计模型做跌倒检测，结合目标跟踪分析视频中的人体关键点，判断是否发生摔倒，可在 CPU 或 GPU 上运行。

核心能力

用 OpenPifPaf 姿态估计模型识别人体关键点
集成行人跟踪器，可在多人场景中检测跌倒
支持视频、RTSP 流和摄像头/USB 摄像头输入
可在 GPU 和 CPU 上运行

容易踩的坑

需要把仓库源码文件复制替换到 openpifpaf 的 site-packages 目录中才能运行；视频/RTSP 路径需先在 config/config.xml 中修改；README 指定 Python 3.7.6 与 OpenPifPaf 0.11.9，版本不匹配可能出问题。

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

用户点评

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README 原文

未翻译

在 GitHub 打开

Fall Detection using Pose Estimation

Introduction

Fall Detection model based on OpenPifPaf

PyPI Library: https://pypi.org/project/openpifpaf/

The detection can run on both GPU and CPU, on multiple videos, RTSP streams, and webcams/USB cameras. Unlike most open-source fall detection models that work on large single subjects, this improved model integrates a person tracker that can detect falls in scenes with more than one person.

Demo Videos

Walking Trip Stubbed Toe Drunk

Video credits: 50 Ways to Fall (Link), ran on a single NVIDIA Quadro P1000

Test Results

UR Fall Detection Dataset (Link), tested on two NVIDIA Quadro GV100s.

Precision: 100%
Recall: 83.33%
F1 Score: 90.91%

Note: Due to lack of available datasets, false positives and true negatives were not tested.

Environment

Ubuntu 18.04 x86_64
Python 3.7.6
Anaconda 3
CUDA 10.2

Usage

Setup Conda Environment

$ conda create --name falldetection_openpifpaf python=3.7.6
$ conda activate falldetection_openpifpaf

Clone Repository

$ git clone https://github.com/cwlroda/falldetection_openpifpaf.git

Download OpenPifPaf 0.11.9 (PyPI)

$ pip3 install openpifpaf

Copy Source Files

$ cd {home_dir}/anaconda3/lib/python3.7/site-packages/openpifpaf
Replace ALL files in that folder with the files in falldetection_openpifpaf

Install Dependencies

$ pip3 install -r requirements.txt

Execution

For videos/RTSP streams, navigate to config/config.xml to edit the video/RTSP stream path, then run:

$ python3 -m openpifpaf.video --show
$ (use --help to see the full list of command line arguments)

For webcams/USB cameras, run:

$ python3 -m openpifpaf.video --source {CAMERA_ID} --show
$ (use --help to see the full list of command line arguments)

Citations

PifPaf: Composite Fields for Human Pose Estimation (Link)

@InProceedings{Kreiss_2019_CVPR,
    author = {Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
    title = {PifPaf: Composite Fields for Human Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on
                Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2019}
}

If you use the dataset above, please cite the following work: (Link)

Bogdan Kwolek, Michal Kepski,
Human fall detection on embedded platform using depth maps and wireless accelerometer,
Computer Methods and Programs in Biomedicine,
Volume 117,
Issue 3,
December 2014,
Pages 489-501,
ISSN 0169-2607