| Skill Level | Area of Focus | Operating System | Platform/Hardware |
|---|---|---|---|
| Advanced | Artificial Intelligence, Computer Vision, Embedded, IoT, Security, Smart Cities | Linux | DragonBoard 410c |
This project is an intelligent lighting design that is meant to control a luminaire based on a computer vision system running on DragonBoard™ 410c from Arrow Electronics. The control manages the luminous intensity and power drive of the luminaire according to the amount of people detected in the environment.
Objective
The main goal of this project is to demonstrate how you can build a high-end Smart Cities solution with the DragonBoard 410c. Efficient use of resources are a benefit of smart lighting.
Materials Required / Parts List / Tools
- DragonBoard 410c boards
- 12V wall adapter
- Micro SD Card (16GB)
- Logitech c920 Webcam
- Mezzanine Sensors Board
- Control Circuit (* build instructions on the source code link)
- Generic 220V LED Lamp/Bulb
- Generic Lamp Fixture
Source Code / Source Examples / Application Executable
Build / Assembly Instructions
In this project you will need some electronics knowledge to build the control board. This board is an interface between the Mezzanine and the lamp.
For the code, you will basically install the libraries on the board and install the application. Detailed step-by-step instructions are in the GitLab source code link.
You will need to install the board onto the lamp that you want to control. Again, some electrical knowledge is needed. After installing the camera, you are ready to run the application.
Usage Instructions
Our application uses the video captured from the camera to identify if there are people in the environment. Point the camera at the region that you wish to control. When you run the project, it will run the computer vision application. If people are detected in the region, the lamp will be activated by the Mezzanine and control board.
Contributors
| Name | Title/Company |
|---|---|
| Augusto | Qualcomm IoT Reference Center - FACENS |
| Felipe | Qualcomm IoT Reference Center - FACENS |
| Lucas Max | Qualcomm IoT Reference Center - FACENS |
| Lucas Mendes | Qualcomm IoT Reference Center - FACENS |
| Luccas Schardt | Qualcomm IoT Reference Center - FACENS |