Cámara con face recognition y tracking + Aplicacion Web y Aplicación móvil para la entrada de datos -- 2
Budget: $1,500 – $3,000 USD
Necesito una cámara que funcione con face recognition y tracking de personas embedded (edge computing) desde una posición top view. La cámara se instalará en el marco de una puerta. Ya tenemos un prototipo en funcionamiento con un módulo ESP32, pero se calienta demasiado y es muy lenta la ejecucion. Necesitamos solucionar los problemas de software y hardware y la integracion de ambas partes en un unico producto.
Además, queremos conectar la cámara con una aplicacion web y una aplicación móvil para la entrada de datos para training de ML.
Requirements:
Include entire hardware solution. ESP-EYE or similar board (small board, not raspberry pi or nvidia jetson).
Using fisheye camera (board compatible) from a top view position as it will be installed on a frame door.
Edge Computing with Person detection and face recognition with high accuracy (more than 95%) and more than 8fps.
People Tracking with directionality. People counting.
Preference C/C++ for better performance.
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
The camera will be connected with a Web App for entry users data and images to train a ML model + Log in functionality
All the code commented.
Log file with the application outputs.
Para más detalles, consulte el documento adjunto.
Además, queremos conectar la cámara con una aplicacion web y una aplicación móvil para la entrada de datos para training de ML.
Requirements:
Include entire hardware solution. ESP-EYE or similar board (small board, not raspberry pi or nvidia jetson).
Using fisheye camera (board compatible) from a top view position as it will be installed on a frame door.
Edge Computing with Person detection and face recognition with high accuracy (more than 95%) and more than 8fps.
People Tracking with directionality. People counting.
Preference C/C++ for better performance.
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
The camera will be connected with a Web App for entry users data and images to train a ML model + Log in functionality
All the code commented.
Log file with the application outputs.
Para más detalles, consulte el documento adjunto.
Related categories:
Mobile App Development
Machine Learning (ML)
Web Development
Computer Vision
Edge Computing