The panoramic USB camera module splicing technology mainly involves the integration and processing of video streams from multiple cameras to generate seamless panoramic images. The following are the key technical points:
Camera calibration and distortion correction: Due to manufacturing, installation and process reasons, the lens has internal distortion (such as radial distortion and tangential distortion) and external distortion. It is necessary to obtain the camera’s internal parameter matrix and distortion coefficient through calibration, and use algorithms (such as fisheye correction) to correct the image, eliminate the influence of distortion, and ensure the visual consistency of the picture.
Image alignment and feature extraction: Before stitching, multiple video streams need to be aligned. By extracting image feature points (such as SIFT and SURF algorithms) and calculating the key point matching relationship, combined with the optical flow method to estimate the image motion displacement, the relative position relationship between images is determined, providing a benchmark for subsequent stitching.
Projection transformation and unified viewing Angle: Due to the differences in installation angles of different cameras, the images may not be on the same projection plane. It is necessary to project the images onto a unified reference plane (such as a plane, cylinder or sphere) through perspective transformation, adjust to a consistent viewing Angle, and then stitch them together to avoid image misalignment caused by viewing Angle differences.
Overlapping area fusion processing: If there are overlapping areas in the images of adjacent cameras, they need to be smoothly transitioned through algorithms such as weighted averaging, median filtering, or Poisson fusion to eliminate the stitching seams. The fusion process requires calculating the fusion width and height, and processing the overlapping areas based on color, brightness and texture features to ensure a natural transition of the stitched image.
Brightness and color balance: Due to differences in cameras and lighting, the brightness of the picture is uneven. It is necessary to correct the internal lighting unevenness of the image through a lighting model, adjust the color deviation, and make the brightness and color of the panoramic picture after stitching consistent, avoiding alternations of light and dark or color differences.
Real-time performance and performance optimization: Panoramic stitching requires processing multiple high-definition video streams, and the algorithm efficiency needs to be optimized. The processing speed can be enhanced by reducing the resolution, parallel computing or hardware acceleration (such as GPU) to ensure smooth images with low latency, meeting the requirements of real-time monitoring.
Dynamic environment adaptability: For dynamic scenes (such as vehicle surround view), the stitching parameters need to be updated in real time to handle the changes in the picture caused by camera shake or target movement, and maintain the stability of stitching. Some systems also integrate motion detection functions to mark the moving areas in the panorama.