A YOLOv5 Nano model was trained in this project and it was used for drone detection. The PyTorch file was then converted to blob format using https://tools.luxonis.com/ for running on the OAK cameras.
This example demonstrates multiple Luxonis OAK-D cameras tracking drone in an indoor environment and showing the position in MATLAB.
The position of the drone is inferred in MATLAB to be displayed in a 3D coordinate system as follows:
The values detected from the OAK camera will be displayed on the terminal
| key | action |
|---|---|
| q | quit |
| d | toggle depth view |
| b | open bird eye view |
python3 -m pip -U pip
python3 -m pip install -r requirements.txt
Before you can run this demo you need to calibrate the cameras. Go to multi-cam-calibration and generate a calibration file for each camera. Make sure that the calibration_data_dir in the config.py is set correctly.
Run the main.py with Python 3.
python3 main.py
Camera's position will appear in the bird's-eye view along with its detected objects and the detected value will transmit to MATLAB via TCP protocol for analysis the coordination.
https://github.com/luxonis/depthai-experiments/tree/master/gen2-multiple-devices