Camera face recognition and directionality tracking + Website and Mobile App for data entry -- 3
Budget: $1,500 – $3,000 USD
The project consists of a working camera with face recognition and people tracking directionality embedded (edge computing) from a top view position. The camera will be a fisheye camera and will be installed on a frame door (see the attached image).
In addition, we want to connect the camera with a website and mobile app for data entry for further ML training.
Requirements:
1. Include entire hardware solution. ESP-EYE or similar board (small board, not raspberry pi or nvidia jetson).
2. Using fisheye camera (board compatible) from a top view position as it will be installed on a frame door.
3. Edge Computing with Person detection and face recognition with high accuracy (more than 95%) and more than 8fps.
4. People Tracking with directionality.
5. People counting.
6. C/C++ for better performance.
7. The camera will be connected with a Mobile App that collects user images with a mobile camera and automatically train/retrain a pre-trained ML model + Log in functionality
8. The camera will be connected with a Web App for entry users data and images to train a ML model + Log in functionality
9. All the code commented.
10. Log file with the application outputs
For more details, see the attached document.
In addition, we want to connect the camera with a website and mobile app for data entry for further ML training.
Requirements:
1. Include entire hardware solution. ESP-EYE or similar board (small board, not raspberry pi or nvidia jetson).
2. Using fisheye camera (board compatible) from a top view position as it will be installed on a frame door.
3. Edge Computing with Person detection and face recognition with high accuracy (more than 95%) and more than 8fps.
4. People Tracking with directionality.
5. People counting.
6. C/C++ for better performance.
7. The camera will be connected with a Mobile App that collects user images with a mobile camera and automatically train/retrain a pre-trained ML model + Log in functionality
8. The camera will be connected with a Web App for entry users data and images to train a ML model + Log in functionality
9. All the code commented.
10. Log file with the application outputs
For more details, see the attached document.
Related categories:
Mobile App Development
Machine Learning (ML)
Web Development
Computer Vision
Edge Computing