CN113506218A - 360-degree video splicing method for multi-compartment ultra-long vehicle type - Google Patents
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Abstract
The invention provides a 360-degree video splicing method for a multi-compartment ultralong vehicle type, which comprises the steps of S1, obtaining image frames shot at the same time in video streams of a plurality of cameras; s2, respectively carrying out image correction processing on each image frame to obtain corrected images; s3, respectively determining to-be-processed pixel points in each corrected image, and then performing adaptive noise reduction processing on the to-be-processed pixel points to obtain noise-reduced images; s4, performing projection transformation processing on each noise reduction image to obtain a projection image; s5, image registration processing is carried out on the projection images corresponding to the two adjacent cameras in sequence to obtain a registration result, and image splicing processing is carried out based on the registration result to obtain a spliced image; and S6, composing the spliced images at the continuous time into an output video. According to the invention, only the self-adaptive noise reduction processing is carried out on the pixel points to be processed, so that the noise reduction processing speed can be effectively increased, and more detailed information can be retained in the noise reduction image.
Description
Technical Field
The invention relates to the field of video splicing, in particular to a 360-degree video splicing method for a multi-compartment ultra-long vehicle type.
Background
With the rapid development of image and computer vision technologies, more and more technologies are applied to the field of automotive electronics, a traditional image-based reversing image system only installs a camera at the tail of a car and can only cover a limited area around the tail of the car, while blind areas around the car and the head of the car undoubtedly increase the hidden danger of safe driving, and collision and scratch events easily occur in narrow and congested urban areas and parking lots. In order to enlarge the visual field of a driver, the driver needs to be capable of sensing 360-degree all-around environment, and a whole set of video images around the whole vehicle are formed through video synthesis processing after the mutual cooperation of a plurality of visual sensors.
Since the noise point has an influence on the video splicing, the image frame generally needs to be denoised in the splicing process, however, the existing denoising method generally directly denoises all the pixels, and obviously, the processing method lengthens the delay of the finally output video frame.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a 360 ° video stitching method for a multi-compartment ultralong vehicle type, comprising:
s1, acquiring the image frames shot at the same time in the video streams of a plurality of cameras;
s2, respectively carrying out image correction processing on each image frame to obtain corrected images;
s3, respectively determining to-be-processed pixel points in each corrected image, and then performing adaptive noise reduction processing on the to-be-processed pixel points to obtain noise-reduced images;
s4, performing projection transformation processing on each noise reduction image to obtain a projection image;
s5, image registration processing is carried out on the projection images corresponding to the two adjacent cameras in sequence to obtain a registration result, and image splicing processing is carried out based on the registration result to obtain a spliced image;
and S6, composing the spliced images at the continuous time into an output video.
Preferably, in the plurality of cameras, an overlapping area exists in video frames obtained by two adjacent cameras.
Preferably, the performing image rectification processing on each image frame to obtain a rectified image includes:
and carrying out distortion correction processing on each image frame to obtain a corrected image.
Preferably, the determining the pixel point to be processed in each rectified image includes:
for a pixel point n in the corrected image, the judgment coefficient adidx of the pixel point n is calculated by the following formulan：
Wherein k belongs to {1,2,3,4}, cs represents a preset constant coefficient, fnAnd fmRespectively representing pixel values of pixel point n and pixel point m, m representing wkThe 8 neighborhoods of n are expressed as pixel points contained in
w1Representing pixel points n in 8 neighbourhood of n4、n5Set of compositions, w2Representing pixel points n in 8 neighbourhood of n2、n7Set of compositions, w3Representing pixel points n in 8 neighbourhood of n1、n8Set of compositions, w4Representing images in 8 neighborhoods of nPrime point n3、n6A set of n corresponding sea plug matrixes SnIs shown as
max indicates that the maximum value is taken,
will adidxnComparing with a preset comparison threshold value if adidxnAnd if the comparison threshold value is larger than the comparison threshold value, taking the pixel point n as a pixel point to be processed.
Preferably, the performing projective transformation processing on each noise-reduced image to obtain a projection image includes:
and carrying out circular column projection transformation on each noise reduction image to obtain a projection image.
Preferably, the image registration processing is performed on the projection images corresponding to two adjacent cameras, and the obtaining of the registration result includes:
respectively using a feature extraction algorithm to obtain feature points in the two projection images;
matching the feature points in the two projected images to obtain a feature point matching pair;
and taking the matched pairs of the characteristic points as a registration result.
According to the embodiment of the invention, the adaptive noise reduction processing is only carried out on the pixel points to be processed, so that more detailed information can be kept in the noise reduction image while the noise reduction processing speed is effectively increased. The existing video splicing method generally does not perform noise reduction processing or directly performs noise reduction processing on all pixel points, and if the noise reduction processing is not performed, noise can affect the subsequent image matching process, so that the image matching result is not accurate enough, and if the noise reduction processing is performed on all the pixel points, obviously, the required processing time can be longer, so that the time for obtaining the output video is too long, and the difference between the shooting time of the picture in the output video and the output time of the output video is too large, so that the picture delay is too large.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram illustrating an exemplary embodiment of a 360 ° video stitching method for a multi-compartment ultra-long vehicle type according to the present invention.
Fig. 2 is a diagram illustrating an exemplary embodiment of a camera mounting manner of the multi-compartment ultra-long vehicle type according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In one embodiment shown in fig. 1, the present invention provides a 360 ° video stitching method for a multi-compartment ultralong vehicle, comprising:
s1, acquiring the image frames shot at the same time in the video streams of a plurality of cameras;
s2, respectively carrying out image correction processing on each image frame to obtain corrected images;
s3, respectively determining to-be-processed pixel points in each corrected image, and then performing adaptive noise reduction processing on the to-be-processed pixel points to obtain noise-reduced images;
s4, performing projection transformation processing on each noise reduction image to obtain a projection image;
s5, image registration processing is carried out on the projection images corresponding to the two adjacent cameras in sequence to obtain a registration result, and image splicing processing is carried out based on the registration result to obtain a spliced image;
and S6, composing the spliced images at the continuous time into an output video.
Specifically, for example, when the multi-compartment ultralong vehicle has 4 compartments, as shown in fig. 2, a person skilled in the art may set 10 cameras to acquire image frames, two cameras are respectively set at the outer side of the center of each compartment, and one camera is respectively set at the head and the tail of the vehicle, so as to achieve 360 ° coverage of the shooting range.
The 10 cameras are respectively a left camera to a left four cameras, a right camera to a right four cameras, and then a front camera and a rear camera are added.
In particular, the camera may be a fisheye camera.
Specifically, the image frames of the continuous time frames are processed to obtain a plurality of spliced images, the spliced images are sorted according to the shooting time of the image frames corresponding to the spliced images, and the spliced images are used as video frames in the output video.
Preferably, in the plurality of cameras, an overlapping area exists in video frames obtained by two adjacent cameras.
The overlapping area is mainly set to facilitate the splicing of video frames.
Preferably, the performing image rectification processing on each image frame to obtain a rectified image includes:
and carrying out distortion correction processing on each image frame to obtain a corrected image.
Specifically, the distortion includes radial distortion and tangential distortion. And respectively carrying out radial distortion correction and tangential distortion correction on the image frame, and then fusing the two correction results to obtain a corrected image.
Preferably, the determining the pixel point to be processed in each rectified image includes:
for a pixel point n in the corrected image, the judgment coefficient adidx of the pixel point n is calculated by the following formulan：
Wherein k belongs to {1,2,3,4}, cs represents a preset constant coefficient, fnAnd fmRespectively representing pixel values of pixel point n and pixel point m, m representing wkThe 8 neighborhoods of n are expressed as pixel points contained in
w1Representing pixel points n in 8 neighbourhood of n4、n5Set of compositions, w2Representing pixel points n in 8 neighbourhood of n2、n7Set of compositions, w3Representing pixel points n in 8 neighbourhood of n1、n8Set of compositions, w4Representing pixel points n in 8 neighbourhood of n3、n6A set of n corresponding sea plug matrixes SnIs shown as
max indicates that the maximum value is taken,
will adidxnComparing with a preset comparison threshold value if adidxnAnd if the comparison threshold value is larger than the comparison threshold value, taking the pixel point n as a pixel point to be processed.
In the above embodiment of the present invention, when the pixel point n is a pixel point to be processed, the determination coefficient is much larger than that of a pixel point not to be processed, and if the pixel point n is a normal pixel point or edgeBoundary pixel point, then adidxnThe value of (c) will be very small and can therefore be identified by setting a comparison threshold. Meanwhile, the boundary pixel points can be prevented from being mistakenly considered as the pixel points to be processed.
Preferably, the performing adaptive noise reduction processing on the pixel point to be processed includes:
if adidxnIf < thre, the pixel point n is denoised in the following way:
if adidxnAnd if the pixel point n is not less than thre, performing noise reduction processing on the pixel point n by adopting the following method:
in the formula (a) anRepresenting the pixel value of a pixel n subjected to noise reduction, un representing the set of pixels in a neighborhood of R multiplied by R of the pixel n, nofun representing the number of pixels contained in un, fhThe pixel value, lod [ n, h, representing a pixel point h in un]Representing the total number of pixels, g, passed by the connection between pixels n and hnAnd ghRespectively representing the gradient amplitudes of the pixel points n and h, wherein thre represents a preset judgment coefficient,
according to the embodiment of the invention, different noise reduction processing modes are respectively adopted for the pixels to be processed with different judgment coefficients, so that the processing mode of the invention has higher pertinence. Specifically, if the judgment coefficient of the pixel point to be processed is smaller, it indicates that the difference between the pixel point to be processed and the surrounding pixel points is smaller, so that the noise reduction processing is performed by directly calculating the average value, if the judgment coefficient of the pixel point to be processed is larger, the difference between the pixel point to be processed and the surrounding pixel points is larger, and the loss of the boundary information of the image is easily caused by adopting a common noise reduction mode, therefore, different coefficients are respectively obtained for the pixel points in the neighborhood through the importance degree, and then, the noise reduction result is obtained through the product of the coefficient and the pixel point, and the processing mode integrates the influence factors of the pixel points in the neighborhood and the pixel points processed currently in two aspects of the difference of the linear distance and the gradient amplitude, can well express the importance degree of different pixel points, and is favorable for improving the accuracy of the noise reduction result while keeping boundary information as much as possible. Meanwhile, a fast noise reduction mode is adopted for the pixel points with smaller judgment coefficients, so that the noise reduction time can be further shortened, and the delay of the output video of the invention is shortened.
Preferably, the performing projective transformation processing on each noise-reduced image to obtain a projection image includes:
and carrying out circular column projection transformation on each noise reduction image to obtain a projection image.
Specifically, the projection system may be a cube projection, SSP projection, TSP projection, or the like, in addition to the cylindrical projection transform.
Preferably, the image registration processing is performed on the projection images corresponding to two adjacent cameras, and the obtaining of the registration result includes:
respectively using a feature extraction algorithm to obtain feature points in the two projection images;
matching the feature points in the two projected images to obtain a feature point matching pair;
and taking the matched pairs of the characteristic points as a registration result.
Specifically, the feature extraction algorithm includes a harris algorithm, a susan algorithm, and the like.
According to the embodiment of the invention, the adaptive noise reduction processing is only carried out on the pixel points to be processed, so that more detailed information can be kept in the noise reduction image while the noise reduction processing speed is effectively increased. The existing video splicing method generally does not perform noise reduction processing or directly performs noise reduction processing on all pixel points, and if the noise reduction processing is not performed, noise can affect the subsequent image matching process, so that the image matching result is not accurate enough, and if the noise reduction processing is performed on all the pixel points, obviously, the required processing time can be longer, so that the time for obtaining the output video is too long, and the difference between the shooting time of the picture in the output video and the output time of the output video is too large, so that the picture delay is too large.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. A360-degree video splicing method for a multi-compartment ultralong vehicle type is characterized by comprising the following steps:
s1, acquiring the image frames shot at the same time in the video streams of a plurality of cameras;
s2, respectively carrying out image correction processing on each image frame to obtain corrected images;
s3, respectively determining to-be-processed pixel points in each corrected image, and then performing adaptive noise reduction processing on the to-be-processed pixel points to obtain noise-reduced images;
s4, performing projection transformation processing on each noise reduction image to obtain a projection image;
s5, image registration processing is carried out on the projection images corresponding to the two adjacent cameras in sequence to obtain a registration result, and image splicing processing is carried out based on the registration result to obtain a spliced image;
and S6, composing the spliced images at the continuous time into an output video.
2. The method as claimed in claim 1, wherein the video frames obtained by two adjacent cameras in the plurality of cameras have overlapping regions.
3. The 360 ° video stitching method for the multi-compartment ultralong vehicle type according to claim 2, wherein the performing image rectification processing on each image frame to obtain a rectified image comprises:
and carrying out distortion correction processing on each image frame to obtain a corrected image.
4. The method for 360 ° video stitching according to claim 1, wherein the determining the pixel points to be processed in each rectified image comprises:
for a pixel point n in the corrected image, the judgment coefficient adidx of the pixel point n is calculated by the following formulan：
Wherein k belongs to {1,2,3,4}, cs represents a preset constant coefficient, fnAnd fmRespectively representing pixel values of pixel point n and pixel point m, m representing wkThe 8 neighborhoods of n are expressed as pixel points contained in
w1Representing pixel points n in 8 neighbourhood of n4、n5Set of compositions, w2Representing pixel points n in 8 neighbourhood of n2、n7Set of compositions, w3Representing pixel points n in 8 neighbourhood of n1、n8Set of compositions, w4Representing pixel points n in 8 neighbourhood of n3、n6Composition ofA sea plug matrix S corresponding to nnIs shown as
max indicates that the maximum value is taken,
will adidxnComparing with a preset comparison threshold value if adidxnAnd if the comparison threshold value is larger than the comparison threshold value, taking the pixel point n as a pixel point to be processed.
5. The 360 ° video stitching method for the multi-compartment ultralong vehicle type according to claim 1, wherein the performing projective transformation on each noise-reduced image to obtain a projected image comprises:
and carrying out circular column projection transformation on each noise reduction image to obtain a projection image.
6. The 360-degree video stitching method for the multi-compartment ultralong vehicle type according to claim 1, wherein image registration processing is performed on the projection images corresponding to two adjacent cameras to obtain a registration result, and the method comprises the following steps:
respectively using a feature extraction algorithm to obtain feature points in the two projection images;
matching the feature points in the two projected images to obtain a feature point matching pair;
and taking the matched pairs of the characteristic points as a registration result.
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