Welcome to the fascinating world of fall detection using pose estimation! Here, we’ll explore how to harness the power of the OpenPifPaf library to create a reliable fall detection system that can operate seamlessly on both complex scenarios and single-user environments.

Introduction

The fall detection model based on OpenPifPaf delivers a robust solution capable of detecting falls in crowded scenes with multiple persons. Unlike traditional models, this innovative approach incorporates a person tracker, enhancing accuracy. Whether you’re using a GPU or CPU, this model ensures you won’t miss a beat, regardless of whether you’re working with videos, RTSP streams, or USB webcams.

Demo Videos

Before jumping into the technical details, let’s take a look at some demo videos that showcase this model in action:

• Walking Trip
• Stubbed Toe
• Drunk

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

Test Results

Using the UR Fall Detection Dataset (Link), the model was tested on two NVIDIA Quadro GV100s and showed impressive performance:

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

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

Environment Setup

To get started, you’ll need the following environment:

• Ubuntu 18.04 x86_64
• Python 3.7.6
• Anaconda 3
• CUDA 10.2

How to Set Up the Fall Detection System

Follow these steps to set up your fall detection system:

1. Setup Conda Environment

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

2. Clone the Repository

git clone https://github.com/cwlrodafalldetection_openpifpaf.git

3. Download OpenPifPaf 0.11.9 (PyPI)

pip3 install openpifpaf

4. Copy Source Files

Navigate to the following directory:

cd home_dir/anaconda3/lib/python3.7/site-packages/openpifpaf

Now, replace ALL files in that folder with the files in the falldetection_openpifpaf directory.

5. Install Dependencies

pip3 install -r requirements.txt

6. Execution

For videos and RTSP streams, you need to edit the video/RTSP stream path in the _config/config.xml file. Then run:

python3 -m openpifpaf.video --show

(Use --help to see the full list of command line arguments.)

For webcams or USB cameras, run:

python3 -m openpifpaf.video --source CAMERA_ID --show

(Again, use --help for more options.)

Troubleshooting

While setting up and running your fall detection model, you might encounter some common issues, such as:

• Camera not detected: Ensure your webcam is properly connected and recognized by your operating system.
• Errors during the installation of dependencies: Double-check your Python version and ensure that your Anaconda setup is correct.
• Performance lags: If you’re running on an older system, consider closing unnecessary applications to free up resources.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.