This project implements a fall detection system using pose estimation techniques and recurrent neural networks (GRU). The system is capable of analyzing video sequences to identify falls in real-time.
notebooks/: Contains Jupyter notebooks for model training and evaluation.src/: Contains the project's source code.models/: Implementations of fall detection models.fall_detection_lstm.py: LSTM model for fall detection.fall_detection_gru.py: GRU model for fall detection.
utils/: Utilities and helper functions.video_detect_falls.py: Functions for fall detection in videos.body.py: Definitions of connections and body parts for pose estimation.
data/: Directory to store training and test data.media/: Contains images and videos used in the README and other documents.
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Clone the repository:
git clone https://github.com/your-username/fall-detection.git cd fall-detection -
Create a virtual environment and install the dependencies:
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install -r requirements.txt
To detect falls in a video, use the following script:
from src.utils.video_detect_falls import video_detect_falls
video_detect_falls(
video_path='data/videos/falls/yoga-fail-fall.mp4', # Change this to the path of the video you want to test
yolo_model_path='yolo11x-pose.pt',
gru_model='models/gru_model.pth',
fall_threshold=.95,
scale_percent=100,
sequence_length=20,
show_pose=True,
record=True,
)