azarmi / birds-eye-view-calibration

Bird’s Eye View Calibration Toolkit

Birds Eye View Calibration Toolkit

The Inverse Perspective Mapping (IPM) is the process of converting a perspective image to a perpendicular top-to-bottom view image, also known as Bird's Eye View Mapping (BEW). This process involves some initial calibration steps. The toolkit in this repository provides a calibration technique using Python and OpenCV which is applicable to both manual and satellite-image-based calibration methods.

The tutorial for using the toolkit is outlined in Guide.pdf.

Requirements

• OS: Windows / Linux / Mac
• Python: 3.8.1 (or above)
• OpenCV: 4.7.0 (or above)
• Numpy: 1.23.5 (or above)

Calibration Methods

Manual Calibration ('Calib_GrndPlane.py')

This script allows manual determination of the ground plane and estimation of BEV calibration points from a video file, as follows:

• Background Extraction: Removing moving objects in the scene.
• ROI Determination: Selecting the region of interest.
• Ground Plane Selection: Marking four points to create a foursquare in the scene.
• Refining Aspect Ratio: Determining pixel-to-meter ratio in two directions.

The process generates a folder with configuration files and images representing each step.

Satellite-based Calibration ('Calib_SatFeature.py')

This script requires a perpendicular satellite image of the location where the video is recorded and involves the following steps:

• Background Extraction: Removing moving objects in the scene.
• ROI Determination: Selecting the region of interest.
• Point Identification: Selecting at least four points in the satellite image and reidentifying them in the video scene.
• Refining Aspect Ratio: Determining pixel-to-meter ratio in two directions.

Similar to the manual calibration, this process generates a folder with configuration files and step-by-step images.

References

• Rezaei, M., Azarmi, M., & Mir, F.M.P. (2023). "3D-Net: Monocular 3D object recognition for traffic monitoring." Expert Systems with Applications, 227, p.120253. Paper | Code | Demo | Code Description

• Rezaei, M., & Azarmi, M. (2020). "Deepsocial: Social distancing monitoring and infection risk assessment in COVID-19 pandemic." Applied Sciences, 10(21), p.7514. Paper | Code | Demo