A method and system for seamless stitching and processing of vehicle-mounted panoramic images

Abstract

The invention discloses a vehicle-mounted panoramic image seamless splicing processing method, which comprises the following steps: respectively acquiring original environment images through cameras arranged around the car; through a pre-generated target image splicing mapping table, querying and obtaining each target image Mapping information corresponding to the target pixel, wherein the mapping information includes at least the serial number of the original environment image and pixel coordinate information; according to the mapping information of each target pixel in the target image, obtain the corresponding serial number on the original environment image Corresponding to the pixel value on the pixel coordinates, and obtaining the final pixel value of each target pixel point on the target image according to the pixel value, and assigning the final pixel value to the corresponding target pixel point. By implementing the present invention, the speed and stitching effect of panorama image mosaic can be improved.

Description

technical field [0001] The invention belongs to the technical field of automotive electronics, and in particular relates to a method and system for seamless splicing and processing of vehicle-mounted panoramic images for parking assistance. Background technique [0002] Panoramic image stitching, as an emerging technology, has developed rapidly in recent years and has attracted more and more attention from researchers. In panoramic visualization assisted parking, panoramic image stitching technology is needed to generate a panoramic view around the vehicle body. Among them, the seamless stitching technology is the core of the panoramic system. The seamless stitching of images is to form a seamless, high-definition image by aligning a series of overlapping images in space, with higher resolution and larger size than a single image. vision. [0003] At present, in the prior art, a region-based stitching method and a feature point-based stitching method are generally used to ...

AI Technical Summary

Problems solved by technology

However, these feature points often need to manually select initial matching points, which reduces the speed of the algorithm and cannot adapt to image fusion with large amounts of data.

The feature-based mosaic method adopts the feature block matching algorithm, that is, the image registration method using the correlation criterion. Since the correlation method is a search method for global optimization, the calculation amount is quite large, and the splicing speed is slow, so it is difficult to guarantee the accuracy of the algorithm. real-time

[0008] In summary, it can be seen that the above two splicing methods have the disadvantages of slow splicing speed and poor real-time performance.

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