CN111199177A - Automobile rearview pedestrian detection alarm method based on fisheye image correction - Google Patents
Automobile rearview pedestrian detection alarm method based on fisheye image correction Download PDFInfo
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Abstract
The invention discloses an automobile rearview pedestrian detection alarm method based on fisheye image correction. The invention comprises the following steps: collecting information behind the vehicle by using a fish-eye lens; extracting an effective region from the collected fisheye image; reprocessing the distortion of the image edge; detecting pedestrians in the image, identifying and segmenting the pedestrians, and predicting moving pedestrians; and judging whether to alarm or not according to the positions of the pedestrians and the tail of the vehicle and the movement trend of the pedestrians. The invention uses the fisheye lens to collect the vehicle running information, thus overcoming the defects of limited visual range and large installation number of the traditional camera; denoising and distorting the fisheye image by combining a plurality of image processing algorithms, so that the pedestrian can be accurately identified; and analyzing the positions and the movement trends of the pedestrians by using a pedestrian recognition algorithm, and finally achieving the effects of prompting and protecting the driving safety of the vehicle.
Description
Technical Field
The invention relates to the field of computer vision and video image processing, in particular to an automobile rearview pedestrian detection alarm method based on fisheye image correction.
Background
Pedestrian Detection (Pedestrian Detection) is the use of computer vision techniques to determine whether a Pedestrian is present in an image or video sequence and to provide accurate positioning. The technology can be combined with technologies such as pedestrian tracking and pedestrian re-identification, and is applied to the fields of artificial intelligence systems, vehicle driving assistance systems, intelligent robots, intelligent video monitoring, human body behavior analysis, intelligent transportation and the like. As one of the pioneering technologies of unmanned driving, a pedestrian detection system has also become a research and development hotspot in recent years, and is generally integrated into a collision prevention system, a camera and an inductor are used to detect pedestrians, and the pedestrian detection system is decelerated and braked in time so as to reduce accident damage, and the pedestrian detection is also paid more and more attention in the fields of intelligent transportation and the like.
The fisheye lens has the function of wide visual angle range, the visual angle can reach 220 degrees or 230 degrees generally, and conditions are created for shooting a large-range scene at a short distance. The fish-eye lens is an optical imaging system with an ultra-large field of view and a large aperture, and compresses the ultra-large field of view of an object to the field of view range required by a conventional lens. The fisheye lens can realize the full airspace containment and the acquisition of full time domain real-time information, and the fisheye lens plays a full role in the fields of photography, meteorology, measurement technology, medical application, safety monitoring and the like.
Due to the characteristic of the fish-eye lens of an ultra-large wide angle, the imaging of the fish-eye lens also has geometric distortion, and the distortion of the fish-eye image is reduced as much as possible by carrying out distortion correction on the image through an image correction algorithm.
The existing pedestrian detection alarm technology is a system for informing a vehicle approaching by a vehicle sound of Toyota corporation. The method comprises the following specific steps:
1. sensing pedestrians or obstacles around the vehicle by using an ultrasonic sensor;
2. if it is sensed that a pedestrian or other object is less than a given distance from the vehicle, the system automatically sounds a sound (whistle) to notify the pedestrian.
The disadvantages of this method are:
1. the sound informing system detects that the detection object is not only a pedestrian, and when an immovable object (such as a tree and a building) is detected, the noise generated by the vehicle is likely to influence the surrounding environment;
2. in many cities in China, the stipulation of forbidding whistling is provided, and the automatic whistling system easily penalizes drivers;
3. non-fisheye cameras typically do not have a 180 ° viewing angle and are less accurate. A plurality of cameras are generally required to be installed for observing the running environment of the vehicle in an all-around manner, so that not only is the cost increased, but also the attractiveness is influenced to a certain extent;
4. installing multiple cameras increases the lateral volume of the module and increases the complexity of system design and computation.
Disclosure of Invention
The invention aims to overcome the defects of the existing method and provides an automobile rearview pedestrian detection alarm method based on fisheye image correction. The scheme of the invention mainly solves two core problems, firstly, the image distortion caused by the vehicle running condition acquired by the fisheye lens is corrected, and the image distortion and the error are reduced; secondly, detecting and predicting pedestrians behind the vehicle according to denoising and distortion processing of the fisheye image, and judging whether to remind a driver according to the moving direction and the distance of the pedestrians.
In order to solve the above problems, the present invention provides an automobile rearview pedestrian detection alarm method based on fisheye image correction, wherein the method comprises:
collecting information behind the vehicle by using a fish-eye lens;
preprocessing, namely extracting an effective area from the collected fisheye image by using a spherical coordinate positioning algorithm;
the preprocessed image is reprocessed aiming at the distortion of the image edge by using a bilinear interpolation algorithm;
detecting pedestrians in the image by using a background frame difference method, identifying and segmenting the pedestrians, and predicting the moving pedestrians by using a gray model;
and judging whether to alarm or not according to the positions of the pedestrians and the tail of the vehicle and the movement trend of the pedestrians.
Preferably, the judging whether to give an alarm according to the positions of the pedestrians and the vehicle tail and the movement trend of the pedestrians specifically comprises:
if the vehicle is in a reversing state, the distance between the pedestrian and the vehicle is not more than four meters, and the alarm is directly started;
if the vehicle is in a reversing state, if the pedestrian is detected to walk to the tail of the vehicle and the distance is not less than five meters, alarming;
if the vehicle is in a reversing state, the vehicle alarms when the distance from the pedestrian to the tail of the vehicle is not more than three meters, and does not alarm under other conditions.
The invention provides an automobile rearview pedestrian detection alarm method based on fisheye image correction, which uses a fisheye lens to collect vehicle running information and overcomes the defects of limited visual range and large installation number of the traditional camera; denoising and distorting the fisheye image by combining a plurality of image processing algorithms, so that the pedestrian can be accurately identified; and analyzing the positions and the movement trends of the pedestrians by using a pedestrian recognition algorithm, and finally achieving the effects of prompting and protecting the driving safety of the vehicle.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a pedestrian detection alarm method according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a pedestrian detection alarm method according to an embodiment of the present invention, and as shown in fig. 1, the method includes:
s1, collecting information behind the vehicle by using the fish-eye lens;
s2, preprocessing, and extracting an effective area of the collected fisheye image by using a spherical coordinate positioning algorithm;
s3, the preprocessed image is reprocessed aiming at the distortion of the image edge by using a bilinear interpolation algorithm;
s4, detecting pedestrians in the image by using a background frame difference method, identifying and segmenting the pedestrians, and predicting the moving pedestrians by using a gray model;
and S5, judging whether to give an alarm according to the positions of the pedestrians and the tail of the vehicle and the movement trend of the pedestrians.
Step S1 is specifically as follows:
the middle part of the tail of the vehicle behind the vehicle is provided with a fisheye lens, and the fisheye lens is used for collecting driving information behind the vehicle. The fisheye lens has a wider visual field, and the distortion effect can be reduced along with the horizontal lines and the number of scenes easy to distinguish, so the fisheye lens is horizontally arranged in the middle of the tail of the vehicle and is parallel to the ground, and the quality of the picture acquired by the fisheye lens is improved.
Step S2 is specifically as follows:
s2-1, establishing a model by using a spherical coordinate positioning algorithm:
the spherical coordinate positioning method is also called longitude mapping method, and is a typical and rapid two-dimensional fisheye image correction algorithm. And after the points on the same longitude are corrected, the corrected coordinates have the same abscissa, and the longitude mapping of the spherical surface is completed by a spherical coordinate positioning method.
S2-2, binarizing the image by using an average value method:
binarization of an image is the process of changing a color RGB image into a pure gray scale image. The average value method is a method in which the luminances of R, G, B three components in a color image are averaged, and the obtained value is output as a gradation value to obtain a gradation image.
S2-3, obtaining an effective area by using a scanning line approximation method:
and obtaining a gray value difference I according to the gray value of each point, and once the value of I is found to be larger than a preset threshold value, considering that the scanning enters an effective area, and stopping searching in the direction.
S2-4, calculating the center and the radius of the effective area:
and after the scanning is finished, calculating to obtain a circumscribed square, and calculating to obtain a center and a radius.
S2-5, obtaining the relation between corresponding pixel points of the target image and the original image:
and (3) performing forward or reverse coordinate transformation by using a spherical coordinate positioning algorithm to obtain the relationship between the initial coordinate and the transformed coordinate:
xhthe abscissa (which is an integer) of the h point in the original image; r is the radius of the effective area of the fisheye, and Y is the ordinate of the k point in the original image; x is the number ofkAre the corrected coordinates. Since digital images cannot store non-integers. For the point of which the calculated point is not an integer, the non-integer pixel point in the original image can be found through inverse mapping from an established corrected image, one or more accurate points in the original image are positioned through a corresponding interpolation method, and the value of the pixel at the position after correction is obtained through the values of the points. Inverse mapping is performed using the following formula:
step S3 is specifically as follows:
after distortion correction is carried out on the fisheye lens image by adopting a spherical coordinate positioning algorithm model, the middle part of the image is completely restored, but the edge part still has certain barrel-shaped distortion. In order to obtain a better correction effect, bilinear gray level interpolation needs to be carried out on the primary corrected image.
Step S4 is specifically as follows:
s4-1, rapidly obtaining a background image by adopting a self-adaptive method:
taking the input 1 st frame image as an original background, carrying out binarization processing on the current inter-frame difference image from the 2 nd frame, and finding a motion area and a non-motion area by using a binary image; the current background image is then updated with the non-moving area portion of the current frame image. Binary image W of current interframe difference imagekIs defined as:
Wk＝1(|Ik(i,j)-Ik-1(i,j)|≥g)
Wk＝0(|Ik(i,j)-Ik-1(i,j)|＜g)
Ik-1(I, j) and Ik(i, j) are respectively the gray values of two continuous frames of images in the image sequence at the image coordinates (i, j), if the maximum peak value in the gray histogram of the frame difference image is M, g is the corresponding gray value at which the right side value of the maximum peak value is 1/10M, and the obtained binary image W is used as the gray valuekFor the original background image Bk-1Updating to obtain the current background image BkComprises the following steps:
Bk(i,j)＝Bk-1(i, j) when WkWhen (i, j) is 1
Bk(i,j)＝(1-α)Ik(1,j)+αBk-1(i, j) when WkWhen (i, j) is 0
Bk-1(i, j) represents the gray value of the original background image at the coordinate (i, j); wk(i, j) represents the gray scale value of the binary image at the coordinates (i, j), and the background update coefficient α is 0.9
S4-2, segmenting the image, and confirming that the pedestrian:
the height and width of the pedestrian generally conform to a certain proportion, the shape of the pedestrian can be approximated to the aspect ratio of the circumscribed rectangle thereof, and according to the segmentation result, the aspect ratio and the shape dispersion degree of the target are easily calculated, so that the pedestrian and the vehicle in the picture are distinguished. The formula is as follows:
R＝H/L
C＝P2/A
H. l, P and A are the height, width, circumference and area of the target, respectively; r is the aspect ratio of the target and C is the shape dispersion.
S4-3, using the gray model GM (1,1) as a motion model, predicting the pedestrian motion:
and processing the historical data by using a gray model to find the motion rule of the pedestrian so as to predict the future state of the pedestrian. The gray model is defined as follows:
the initialization variable U is a nonnegative monotonous original data sequence of a pedestrian prediction object, and consists of measurement quantities in n pictures:
U0＝(u0(1),u0(2),u0(3),...,u0(n),)
generating a new sequence by accumulation:
U1＝(u1(1),u1(2),u1(3),...,u1(n),)
the corresponding differential equation for the gray model GM (1,1) model is:
and a and b are constant coefficients corresponding to the differential equation.
wherein:
after the estimation parameters are obtained, the following prediction model can be obtained by solving the differential equation:
according to the formula, the GM (1,1) model can predict the next position of the pedestrian only by three pieces of position information of the pedestrian, an interested area is established according to the predicted position, the new pedestrian position is detected, historical data is updated in time by the obtained position information, the latest change rule of the pedestrian motion can be quickly tracked, and the accurate prediction of the pedestrian position is made.
Step S5 is specifically as follows:
if the vehicle is in a reversing state, the distance between the pedestrian and the vehicle is not more than four meters, and the alarm is directly started; if the vehicle is in a reversing state, if the pedestrian is detected to walk to the tail of the vehicle and the distance is not less than five meters, alarming; if the vehicle is in a reversing state, the vehicle alarms when the distance from the pedestrian to the tail of the vehicle is not more than three meters, and does not alarm under other conditions.
According to the automobile rearview pedestrian detection alarm method based on fisheye image correction, the fisheye lens is used for collecting vehicle running information, and the defects that a traditional camera is limited in visual range and large in installation quantity are overcome; denoising and distorting the fisheye image by combining a plurality of image processing algorithms, so that the pedestrian can be accurately identified; and analyzing the positions and the movement trends of the pedestrians by using a pedestrian recognition algorithm, and finally achieving the effects of prompting and protecting the driving safety of the vehicle.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or the like.
In addition, the automobile rearview pedestrian detection alarm method based on fisheye image correction provided by the embodiment of the invention is described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (2)
1. A car rearview pedestrian detection alarm method based on fisheye image correction is characterized by comprising the following steps:
collecting information behind the vehicle by using a fish-eye lens;
preprocessing, namely extracting an effective area from the collected fisheye image by using a spherical coordinate positioning algorithm;
the preprocessed image is reprocessed aiming at the distortion of the image edge by using a bilinear interpolation algorithm;
detecting pedestrians in the image by using a background frame difference method, identifying and segmenting the pedestrians, and predicting the moving pedestrians by using a gray model;
and judging whether to alarm or not according to the positions of the pedestrians and the tail of the vehicle and the movement trend of the pedestrians.
2. The method as claimed in claim 1, wherein the determining whether to alarm according to the positions of the pedestrians and the vehicle tail and the movement trend of the pedestrians comprises:
if the vehicle is in a reversing state, the distance between the pedestrian and the vehicle is not more than four meters, and the alarm is directly started;
if the vehicle is in a reversing state, if the pedestrian is detected to walk to the tail of the vehicle and the distance is not less than five meters, alarming;
if the vehicle is in a reversing state, the vehicle alarms when the distance from the pedestrian to the tail of the vehicle is not more than three meters, and does not alarm under other conditions.
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| CN112216067A (en) * | 2020-09-07 | 2021-01-12 | 邢台林樾科技有限公司 | Image processing method based on vehicle-mounted wide-angle camera |
| CN112329552A (en) * | 2020-10-16 | 2021-02-05 | 爱驰汽车（上海）有限公司 | Obstacle detection method and device based on automobile |
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