Description: The integration of the YOLOv7 Fall Detection Model with NVIDIA's DeepStream SDK 7.0 enables real-time monitoring and alerting for fall incidents. This model leverages advanced object detection capabilities to accurately identify when a person falls, using keypoint analysis of body movements.

Author: Basil Shaji

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Last Updated: Oct-01-2024

Organization: Karunya Institute of Technology and Sciences

Reference Page: https://github.com/myselfbasil/DeepStream-Yolo

Here, I will be demonstrating the entire process for running a deepstream app for Fall Detection

Note: For other versions of yolo models also, follow the same proceedures :)

1. Initial Setup:

Download the YOLOv7 repo and install the requirements

git clone <https://github.com/WongKinYiu/yolov7.git>
cd yolov7
pip3 install onnx onnxsim onnxruntime

Copy appropriate conversor file for your model from the link her

https://github.com/myselfbasil/DeepStream-Yolo/tree/master/utils

Here, Copy the export_yoloV7.py file from DeepStream-Yolo/utils directory to the yolov7 folder.

2. Download the model

Note: If you have your own model, paste it or download the .pt file from YOLOv7 releases (example for YOLOv7)

wget <https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt>

Reparameterize your model (for custom models)

Custom YOLOv7 models cannot be directly converted to engine file. Therefore, you will have to reparameterize your model using the code here. Make sure to convert your custom checkpoints in YOLOv7 repository, and then save your reparmeterized checkpoints for conversion in the next step.