This is the offical repository of our project
monifor the hackathon Hackathon Thurgau 2023. The project was created and developed during the hackathon competition.The project aims to provide a solution for the
Challenge 3: occupancy measurement of a store. This was achieved by using a combination of computer vision and a cnn model (for ReID) to detect and track people in a store and their movement. The data is then used for further analysis and visualizations.
moni
├─── conf Configuration files
| └─── example-config.yml Example configuration file
├─── examples Configuration files
| └─── homography.ipynb Jupyter Notebook for homography show case
├─── github-content Images for the README.md
| |─── tech.md Technical documentation how to use moni
| └─── mermaid_chart.txt Mermaid chart of the moni architecture
├─── Yolov7_StrongSORT_OSNet Submodule from: mikel-brostrom
├─── .gitignore Gitignore file
├─── .gitmodules Gitmodules file
├─── Dockerfile Dockerfile to containerize moni
├─── README.md README.md
├─── main.py Moni main python script
├─── runner_utils.py Util functions which are used by moni
├─── runner.py Runner function which does the processing work
├─── requirements.txt Requirements file
└─── docker-compose.yml Docker compose file to spin up the whole moni platform
This is a demo of the running application with 3 different views:
Video Source: EPFL Labs