Add:Using_YOLOv8_and_AI_Box_for_fall_climbing_and_tracking_detection

Author: LJ-Hao

articles/Chapter_3-Computer_Vision_Projects_and_Practical_Applications/Make_Your_Own_Web_Application_with_Hailo_and_Using_Flask.md

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articles/Chapter_3-Computer_Vision_Projects_and_Practical_Applications/Using_YOLOv8_and_AI_Box_for_fall_climbing_and_tracking_detection.md

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+
+# Using YOLOv8 and AI Box for all climbing and tracking detection
+
+
+## Introduction
+
+This project will guide you on how to use the [reComputed AI Box](https://www.seeedstudio.com/reComputer-AI-R2140-12-p-6431.html) or [reComputed Industry AI Box](https://www.seeedstudio.com/reComputer-AI-Industrial-R2145-12-p-6486.html) to implement fall detection, climbing detection, and tracking detection using the YOLOv8 model.
+
+
+## Hardware prepare
+
+|                                               reCompuer AI Box                                              |                                               reComputer Industry AI Box                                               |
+| :----------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------: |
+| ![Raspberry Pi AI Kit](https://media-cdn.seeedstudio.com/media/catalog/product/cache/bb49d3ec4ee05b6f018e93f896b8a25d/i/m/image114993560.jpeg) | ![reComputer R1100](https://media-cdn.seeedstudio.com/media/catalog/product/cache/bb49d3ec4ee05b6f018e93f896b8a25d/2/-/2-114993595-recomputer-ai-industrial-r2135-12.jpg) |
+| [**Purchase Now**](https://www.seeedstudio.com/reComputer-AI-R2130-12-p-6368.html?utm_source=PiAICourse&utm_medium=github&utm_campaign=Course) | [**Purchase Now**](https://www.seeedstudio.com/reComputer-AI-Industrial-R2135-12-p-6432.html?utm_source=PiAICourse&utm_medium=github&utm_campaign=Course) |
+
+> 💡 **Note**: This project requires a reComputer AI Box or a reComputer Industry AI Box.
+
+
+##  Fall down detection
+
+### Install the runtime environment
+
+```bash
+sudo apt update && sudo apt full-upgrade -y && sudo apt install hailo-all
+```
+
+### Download the project
+
+```bash
+git clone https://github.com/Seeed-Projects/fall_detection_with_AIBox.git
+cd fall_detection_with_AIBox
+```
+
+### Prepare the python environment
+
+```bash
+python -m venv .env --system-site-packages  && source .env/bin/activate
+pip install -r requirements.txt
+```
+
+### Run the project
+```bash
+python app.py -i ./falldown_test.mp4 -n ./yolov8n.hef --show-fps -l ./common/coco.txt
+```
+
+### Result
+
+
+
+## Fence climbing
+
+### Install the runtime environment
+
+```bash
+sudo apt update && sudo apt full-upgrade -y && sudo apt install hailo-all
+```
+
+### Download the project
+
+```
+git clone https://github.com/Seeed-Projects/cross_fence_with_AIBox.git
+cd cross_fence_with_AIBox
+```
+
+### Prepare the python environment
+
+```bash
+python -m venv .env --system-site-packages  && source .env/bin/activate
+pip install -r requirements.txt
+```
+
+### Run the project
+
+```bash
+python app.py -i ./scale_the_fence.mp4 -n ./yolov8n.hef --show-fps -l ./common/coco.txt 
+```
+
+### Result
+
+<iframe width="800" height="400" src="https://www.youtube.com/embed/_-89czNbZ_M?list=PLpH_4mf13-A3Wm6hJp7JeT4DD9NXXUAca" title="Seeed Raspberry Pi AI Box Helps in Abnormal Event Detection: Fence Climbing" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## Tracking 
+
+### Install the runtime environment
+
+```bash
+sudo apt update && sudo apt full-upgrade -y && sudo apt install hailo-all
+```
+
+### Download the project
+
+```bash
+git clone https://github.com/Seeed-Projects/track_people_with_AIBox.git
+cd fall_detection_with_AIBox
+```
+
+### Prepare the python environment
+
+```bash
+python -m venv .env --system-site-packages  && source .env/bin/activate
+pip install -r requirements.txt
+```
+
+### Run the project
+
+```bash
+python app.py -i ./people-walking.mp4 -n ./yolov8n.hef --show-fps -l ./common/coco.txt --track
+```
+
+### result 
+
+<iframe width="800" height="400" src="https://www.youtube.com/embed/xoeMcaG_FxE?list=PLpH_4mf13-A3Wm6hJp7JeT4DD9NXXUAca" title="reComputer Object Detection and Tracking: The Role of YOLOv8 in Shaping Results" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>