Highway high-order video monitoring site selection evaluation method and device

Abstract

The invention relates to the field of expressway monitoring, and discloses an expressway high-order video monitoring site selection assessment method and device, and the method comprises the steps: determining a plurality of to-be-assessed high-order video monitoring sites of a target road; acquiring live-action map information of the target road; determining at least one evaluation influence factor corresponding to each high-order video monitoring site based on the live-action map information; and based on the evaluation impact factors, evaluating each high-order video monitoring site to obtain an evaluation result of each high-order video monitoring site. Visibly, the method can intelligently analyze the plurality of evaluation impact factors of the high-order video monitoring site to be evaluated based on the live-action map information of the expressway, obtains the corresponding evaluation result, improves the site selection efficiency and accuracy of the high-order video monitoring, and facilitates the construction of the high-order video monitoring of the expressway.

Description

Technical Field

[0001] The present invention relates to the field of highway monitoring, and in particular to a method and a device for evaluating the site selection of high-position video monitoring of a highway. Background Art

[0002] With the improvement of social and economic levels, the mileage and traffic volume of highways continue to grow. As of February 2023, the total mileage of highways in my country has reached 177,000 kilometers, and the number of motor vehicles in my country has reached 435 million by the end of 2023. The characteristics of long mileage, large traffic volume and high speed of highways increase the risk of traffic accidents, and traffic safety issues are becoming increasingly serious. The application of highway video surveillance systems helps to prevent highway accidents and determine accident responsibilities. The existing high-speed monitoring equipment is mainly low-mounted video cameras (such as those installed on gantry). Due to the low installation height of the camera, it is easy to be blocked by objects such as green belts and signs, resulting in a short effective monitoring distance and a narrow field of view. The maintenance difficulty is also high, which makes it difficult to meet the requirements of the comprehensive construction of a "visible, measurable, controllable and serviceable" highway operation monitoring system. In response to this problem, some domestic highways have already used tower base stations along the line to build high-position video surveillance sites. However, there is a lack of a complete set of specifications for determining the construction plan of high-position video surveillance sites. There is a certain degree of subjectivity, one-sidedness and limitations, which leads to low efficiency and accuracy in site selection for high-position video surveillance on highways, which is not conducive to the construction of high-position video surveillance on highways. It can be seen that how to provide a high-position video surveillance site selection evaluation method for highways as a site selection basis to improve the accuracy and efficiency of site selection is particularly important. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for evaluating the site selection of high-altitude video surveillance on highways, which can intelligently analyze multiple evaluation influencing factors of the high-altitude video surveillance site to be evaluated based on the real-life map information of the highway, and obtain evaluation results, thereby improving the efficiency and accuracy of site selection for high-altitude video surveillance, thereby facilitating the construction of high-altitude video surveillance on highways.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for evaluating the site selection of high-position video surveillance of a highway, the method comprising:

[0005] Determine the locations of multiple high-position video surveillance stations on the target road to be evaluated;

[0006] Acquire real-view map information of the target road;

[0007] Determine at least one evaluation impact factor corresponding to each of the high-level video surveillance stations based on the real-life map information;

[0008] Based on the evaluation influencing factors, each of the high-position video surveillance station sites is evaluated to obtain an evaluation result of each of the high-position video surveillance station sites; the evaluation result of each of the high-position video surveillance station sites is used as a basis for site selection.

[0009] As an optional embodiment, in the first aspect of the present invention, all the evaluation influencing factors corresponding to each of the high-level video surveillance stations include at least one of the effective visible length of the road surface, the degree of road surface obstruction, the viewing angle, the visible ratio of road lane lines, the visible ratio of slopes, the visible ratio of bridges and the visible ratio of piers; the effective visible length of the road surface represents the road surface length corresponding to the effective visible road surface that can be identified by the computer program; the degree of road surface obstruction represents the ratio of the area of ​​the effective visible road surface obstructed by objects to the total area of ​​the target road within the effective visible road surface range; the viewing angle represents the angle between the road surface normal of the target road and the main optical axis of the camera of the high-level video surveillance station; the visible ratio of road lane lines represents the ratio of the visible length of road lane lines to the total length of road lane lines within the effective visible road surface range; the visible ratio of slopes represents the ratio of the visible area of ​​slopes to the total area of ​​slopes; the visible ratio of bridges represents the ratio of the visible area of ​​bridges to the total area of ​​bridges; the visible ratio of piers represents the ratio of the visible area of ​​piers to the total area of ​​piers.

[0010] As an optional implementation, in the first aspect of the present invention, each of the evaluation impact factors has a corresponding factor weight parameter;

[0011] And, based on the evaluation influencing factor, each of the high-position video surveillance sites is evaluated to obtain an evaluation result of each of the high-position video surveillance sites, including:

[0012] For each of the high-position video surveillance station sites, determining an evaluation grade of the high-position video surveillance station site based on each of the evaluation impact factors and the factor weight parameter of the evaluation impact factor; the evaluation grade is used to evaluate whether the site selection of the high-position video surveillance station site is feasible in the form of a grade;

[0013] For each of the high-position video surveillance sites, the high-position video surveillance site is evaluated according to the evaluation level of the high-position video surveillance site to obtain an evaluation result of the high-position video surveillance site.

[0014] As an optional implementation, in the first aspect of the present invention, determining at least one evaluation impact factor corresponding to each of the high-level video surveillance station locations based on the real-life map information includes:

[0015] For each of the high-position video surveillance sites, at least one evaluation influencing factor is determined based on the site map information of the high-position video surveillance site; the site map information is the map information corresponding to the road section in the real-life map information that matches the high-position video surveillance site.

[0016] As an optional implementation, in the first aspect of the present invention, each of the evaluation impact factors has a corresponding target level set; the target level set is used to determine the degree of fit between the evaluation impact factor and the high-level video surveillance station site corresponding to the evaluation impact factor; the target level set includes at least two target levels;

[0017] And, for each of the high-position video surveillance sites, determining the evaluation level of the high-position video surveillance site based on each of the evaluation impact factors and the factor weight parameter of the evaluation impact factor includes:

[0018] For each of the evaluation impact factors corresponding to each of the high-position video surveillance sites, calculating, based on the evaluation impact factor, a level evaluation parameter at each of the target levels of the evaluation impact factor;

[0019] For each of the high-level video surveillance sites, based on the factor weight parameter of each of the corresponding evaluation impact factors, weighted sum of the level evaluation parameters of all the corresponding evaluation impact factors at each of the target levels is performed to obtain the weighted evaluation parameters of the high-level video surveillance site at each of the target levels;

[0020] For each of the high-level video surveillance sites, the weighted evaluation parameters of the high-level video surveillance site at each of the target levels are numerically sorted, and the evaluation level of the high-level video surveillance site is determined based on the target level corresponding to the weighted evaluation parameter with the highest value.

[0021] As an optional implementation, in the first aspect of the present invention, the target level includes a first target level, a second target level and a third target level;

[0022] And, for each of the evaluation impact factors corresponding to each of the high-position video surveillance sites, calculating the level evaluation parameter of each of the target levels of the evaluation impact factor based on the evaluation impact factor, including:

[0023] For each of the evaluation impact factors of each of the high-position video surveillance sites, calculating the level evaluation parameters of the first target level, the second target level, and the third target level of the evaluation impact factor based on the evaluation impact factor;

[0024] The calculation formula of the level evaluation parameter of the first target level is as follows:

[0025]

[0026] U A(x) represents the grade evaluation parameter; x represents the evaluation influence factor, a is the first critical value corresponding to the evaluation influence factor; b is the second critical value corresponding to the evaluation influence factor; a < b; the first critical value and the second critical value are determined based on each evaluation influence factor;

[0027] The grade evaluation parameter of the second target grade is calculated as follows:

[0028]

[0029] U A(x) represents the grade evaluation parameter; x represents the evaluation influence factor, a is the first critical value corresponding to the evaluation influence factor; b is the second critical value corresponding to the evaluation influence factor; c is the third critical value corresponding to the evaluation influence factor; d is the fourth critical value corresponding to the evaluation influence factor; c < a < b < d; the first critical value, the second critical value, the third critical value and the fourth critical value are determined based on each evaluation influence factor;

[0030] The grade evaluation parameter of the third target grade is calculated as follows:

[0031]

[0032] U A(x) represents the grade evaluation parameter; x represents the evaluation influence factor, a is the first critical value corresponding to the evaluation influence factor; b is the second critical value corresponding to the evaluation influence factor; a < b; the first critical value and the second critical value are determined based on each evaluation influence factor.

[0033] As an optional implementation manner, in the first aspect of the present invention, before obtaining the real scene map information of the target road, the method further includes:

[0034] Collect the real image of the target road by means of drone oblique photography;

[0035] Determine whether there is a near-field blind spot in the real image of the road section in the target road that matches the high-position video surveillance station address. When the judgment result is yes, perform a blind spot filling operation on the real image of the road section that matches the high-position video surveillance station address based on a manual collection method to obtain target road image data; the near-field blind spot is a near-field area that cannot be collected when the real image is collected by the drone in an oblique photography method; the target road image data includes real images of the target road collected from multiple angles and pixel points corresponding to the real images;

[0036] The target road image data is subjected to multi-view stereo matching and pixel points representing the same position in the target road image data are extracted to perform aerial triangulation to obtain a three-dimensional point cloud corresponding to the target road image data; the multi-view stereo matching means matching the real images of the target road collected from multiple angles with each other;

[0037] Based on the three-dimensional point cloud, a three-dimensional space framework model matching the three-dimensional point cloud is determined; the three-dimensional point cloud is fused with the three-dimensional space framework model to obtain real-view map information of the target road.

[0038] The second aspect of the present invention discloses a high-position video surveillance site selection and evaluation device for a highway, the device comprising:

[0039] A determination module, used to determine multiple high-position video surveillance station locations to be evaluated on a target road;

[0040] An acquisition module, used to acquire real-view map information of the target road;

[0041] The determination module is further used to determine at least one evaluation impact factor corresponding to each of the high-position video surveillance stations based on the real-scene map information;

[0042] An evaluation module is used to evaluate each of the high-position video surveillance sites based on the evaluation influencing factors to obtain an evaluation result of each of the high-position video surveillance sites; the evaluation result of each of the high-position video surveillance sites is used as a basis for site selection.

[0043] As an optional embodiment, in the second aspect of the present invention, all the evaluation influencing factors corresponding to each of the high-level video surveillance stations include at least one of the effective visible length of the road surface, the degree of road surface obstruction, the viewing angle, the visible ratio of road lane lines, the visible ratio of slopes, the visible ratio of bridges and the visible ratio of piers; the effective visible length of the road surface represents the road surface length corresponding to the effective visible road surface that can be identified by the computer program; the degree of road surface obstruction represents the ratio of the area of ​​the effective visible road surface obstructed by objects to the total area of ​​the target road within the effective visible road surface range; the viewing angle represents the angle between the road surface normal of the target road and the main optical axis of the camera of the high-level video surveillance station; the visible ratio of road lane lines represents the ratio of the visible length of road lane lines to the total length of road lane lines within the effective visible road surface range; the visible ratio of slopes represents the ratio of the visible area of ​​slopes to the total area of ​​slopes; the visible ratio of bridges represents the ratio of the visible area of ​​bridges to the total area of ​​bridges; the visible ratio of piers represents the ratio of the visible area of ​​piers to the total area of ​​piers.

[0044] As an optional implementation, in the second aspect of the present invention, each of the evaluation impact factors has a corresponding factor weight parameter;

[0045] And, the evaluation module evaluates each of the high-position video surveillance sites based on the evaluation influencing factors, and a specific method for obtaining the evaluation result of each of the high-position video surveillance sites includes:

[0046] For each of the high-position video surveillance station sites, determining an evaluation grade of the high-position video surveillance station site based on each of the evaluation impact factors and the factor weight parameter of the evaluation impact factor; the evaluation grade is used to evaluate whether the site selection of the high-position video surveillance station site is feasible in the form of a grade;

[0047] For each of the high-position video surveillance sites, the high-position video surveillance site is evaluated according to the evaluation level of the high-position video surveillance site to obtain an evaluation result of the high-position video surveillance site.

[0048] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines at least one evaluation impact factor corresponding to each of the high-position video surveillance station locations based on the real-view map information includes:

[0049] For each of the high-position video surveillance sites, at least one evaluation influencing factor is determined based on the site map information of the high-position video surveillance site; the site map information is the map information corresponding to the road section in the real-life map information that matches the high-position video surveillance site.

[0050] As an optional implementation, in the second aspect of the present invention, each of the evaluation impact factors has a corresponding target level set; the target level set is used to determine the degree of fit between the evaluation impact factor and the high-level video surveillance station site corresponding to the evaluation impact factor; the target level set includes at least two target levels;

[0051] And, the evaluation module determines, for each of the high-position video surveillance sites, a specific manner of determining the evaluation level of the high-position video surveillance site based on each of the evaluation impact factors and the factor weight parameter of the evaluation impact factor, including:

[0052] For each of the evaluation impact factors corresponding to each of the high-position video surveillance sites, calculating, based on the evaluation impact factor, a level evaluation parameter at each of the target levels of the evaluation impact factor;

[0053] For each of the high-level video surveillance sites, based on the factor weight parameter of each of the corresponding evaluation impact factors, weighted sum of the level evaluation parameters of all the corresponding evaluation impact factors at each of the target levels is performed to obtain the weighted evaluation parameters of the high-level video surveillance site at each of the target levels;

[0054] For each of the high-level video surveillance sites, the weighted evaluation parameters of the high-level video surveillance site at each of the target levels are numerically sorted, and the evaluation level of the high-level video surveillance site is determined based on the target level corresponding to the weighted evaluation parameter with the highest value.

[0055] As an optional implementation, in the second aspect of the present invention, the target level includes a first target level, a second target level and a third target level;

[0056] And, the specific manner in which the evaluation module calculates the level evaluation parameter at each target level of the evaluation impact factor based on each evaluation impact factor corresponding to each high-position video surveillance station site includes:

[0057] For each of the evaluation impact factors of each of the high-position video surveillance sites, calculating the level evaluation parameters of the first target level, the second target level, and the third target level of the evaluation impact factor based on the evaluation impact factor;

[0058] The calculation formula of the level evaluation parameter of the first target level is as follows:

[0059]

[0060] U A(x)represents the grade evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; a < b; the first critical value and the second critical value are determined based on each evaluation impact factor;

[0061] The grade evaluation parameter of the second target grade, the calculation formula is as follows:

[0062]

[0063] U A(x) represents the grade evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; c is the third critical value corresponding to the evaluation impact factor; d is the fourth critical value corresponding to the evaluation impact factor; c < a < b < d; the first critical value, the second critical value, the third critical value and the fourth critical value are determined based on each evaluation impact factor;

[0064] The grade evaluation parameter of the third target grade, the calculation formula is as follows:

[0065]

[0066] U A(x) represents the grade evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; a < b; the first critical value and the second critical value are determined based on each evaluation impact factor.

[0067] As an optional implementation manner, in the second aspect of the present invention, before the acquisition module acquires the real scene map information of the target road, the device further includes:

[0068] An acquisition module, configured to acquire the real image of the target road by means of drone oblique photography;

[0069] A judgment module, configured to judge whether there is a near-view blind area in the real image of the section of the target road that matches the high-position video monitoring station site. When the judgment result is yes, perform a blind area filling operation on the real image of the section of the target road that matches the high-position video monitoring station site based on the manual acquisition method to obtain the target road image data; the near-view blind area is a near-view area that cannot be acquired when the real image is acquired by the drone oblique photography method; the target road image data includes the real images of the target road acquired from multiple angles and the pixel points corresponding to the real images;

[0070] A computing module, used for performing multi-view stereo matching on the target road image data and extracting pixel points representing the same position in the target road image data to perform aerial triangulation to obtain a three-dimensional point cloud corresponding to the target road image data; the multi-view stereo matching means matching the real images of the target road collected from multiple angles with each other;

[0071] A fusion module is used to determine a three-dimensional space framework model that matches the three-dimensional point cloud based on the three-dimensional point cloud; and fuse the three-dimensional point cloud with the three-dimensional space framework model to obtain real-life map information of the target road.

[0072] A third aspect of the present invention discloses another highway high-position video monitoring site selection and evaluation device, the device comprising:

[0073] A memory storing executable program code;

[0074] a processor coupled to the memory;

[0075] The processor calls the executable program code stored in the memory to execute the highway high-position video surveillance site selection and evaluation method disclosed in the first aspect of the present invention.

[0076] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the highway high-position video surveillance site selection and evaluation method disclosed in the first aspect of the present invention.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] In the embodiment of the present invention, multiple high-level video surveillance station sites to be evaluated on the target road are determined; the real-life map information of the target road is obtained; at least one evaluation influencing factor corresponding to each high-level video surveillance station site is determined based on the real-life map information; and each high-level video surveillance station site is evaluated based on the evaluation influencing factor to obtain an evaluation result for each high-level video surveillance station site. It can be seen that the implementation of the present invention can intelligently analyze multiple evaluation influencing factors of the high-level video surveillance station sites to be evaluated based on the real-life map information of the highway, and obtain corresponding evaluation results, thereby improving the efficiency and accuracy of site selection for high-level video surveillance, and thus facilitating the construction of high-level video surveillance on highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0080] Figure 1 It is a flow chart of a method for evaluating site selection for high-position video surveillance of a highway disclosed in an embodiment of the present invention;

[0081] Figure 2 It is a flow chart of another method for evaluating the site selection of high-position video surveillance of a highway disclosed in an embodiment of the present invention;

[0082] Figure 3 It is a structural schematic diagram of a high-position video monitoring site selection and evaluation device for a highway disclosed in an embodiment of the present invention;

[0083] Figure 4 It is a structural schematic diagram of another highway high-position video monitoring site selection and evaluation device disclosed in an embodiment of the present invention;

[0084] Figure 5 It is a structural schematic diagram of another highway high-position video monitoring site selection and evaluation device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0087] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0088] The present invention discloses a method and device for evaluating the site selection of high-position video surveillance on expressways. Based on the real-life map information of the expressway, multiple evaluation influencing factors of the high-position video surveillance site to be evaluated are intelligently analyzed, and corresponding evaluation results are obtained, thereby improving the efficiency and accuracy of site selection for high-position video surveillance, thereby facilitating the construction of high-position video surveillance on expressways.

[0089] Embodiment 1

[0090] See also Figure 1 , Figure 1 The present invention discloses a method for evaluating the site selection of a high-level video surveillance system for a highway. Figure 1 The described highway high-position video surveillance site selection evaluation method can be applied to a highway high-position video surveillance site selection evaluation device, and the highway high-position video surveillance site selection evaluation device can be integrated in a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the highway high-position video surveillance site selection and evaluation method may include the following operations:

[0091] 101. Determine the locations of multiple high-level video surveillance stations to be evaluated on the target road;

[0092] In an embodiment of the present invention, the high-position video surveillance station may be installed on a tower base station along the highway; further, the high-position video surveillance installed at the high-position video surveillance station may be integrated with the directional millimeter-wave radar in the high-position scene to realize full-time domain monitoring of the highway.

[0093] 102. Obtaining real-view map information of the target road;

[0094] 103. Determine at least one evaluation impact factor corresponding to each high-position video surveillance station based on the real-scene map information;

[0095] 104. Based on the evaluation impact factor, each high-position video surveillance station site is evaluated to obtain an evaluation result of each high-position video surveillance station site;

[0096] In the embodiment of the present invention, the evaluation result of each high-position video monitoring station site is used as a basis for site selection.

[0097] It can be seen that the implementation of the method described in the embodiment of the present invention can intelligently analyze multiple evaluation influencing factors of the high-altitude video surveillance site to be evaluated based on the real-life map information of the highway, and obtain corresponding evaluation results, thereby improving the efficiency and accuracy of site selection for high-altitude video surveillance, which is beneficial to the construction of high-altitude video surveillance on highways.

[0098] In an optional embodiment, all evaluation influencing factors corresponding to each high-position video surveillance station include at least one of the effective visible length of the road surface, the degree of road surface obstruction, the viewing angle, the visible ratio of road lane lines, the visible ratio of slopes, the visible ratio of bridges and the visible ratio of piers; the effective visible length of the road surface represents the road surface length corresponding to the effective visible road surface that can be identified by the computer program; the degree of road surface obstruction represents the ratio of the area of ​​the effective visible road surface obstructed by objects within the effective visible road surface range to the total area of ​​the target road; the viewing angle represents the angle between the road surface normal of the target road and the main optical axis of the camera of the high-position video surveillance station; the visible ratio of road lane lines represents the ratio of the visible length of road lane lines to the total length of road lane lines within the effective visible road surface range; the visible ratio of slopes represents the ratio of the visible area of ​​slopes to the total area of ​​slopes; the visible ratio of bridges represents the ratio of the visible area of ​​bridges to the total area of ​​bridges; the visible ratio of piers represents the ratio of the visible area of ​​piers to the total area of ​​piers.

[0099] In this optional embodiment, according to the characteristics of the expressway and the monitoring center's needs for monitoring abnormal events, in addition to considering the effective visible length of the road surface, the clarity of the lane lines, the degree of road surface obstruction, etc., it is also necessary to monitor key objects such as dangerous points (such as slopes, bridges, etc.). Therefore, the site selection for each point must comprehensively consider various influencing factors (evaluation influencing factors) such as the road surface, key monitoring objects, and terrain, and each influencing factor (evaluation influencing factor) needs to be quantitatively scored, and different weights are set according to the priority of the influencing factor (evaluation influencing factor).

[0100] In this optional embodiment, the effective visible length of the road surface is the lane line on the road surface, which can be clearly seen by the naked eye of the target such as the vehicle, or the effective visible length of the road surface that can be recognized by the computer, rather than the length of the road surface seen in the field of view of the point. This requires combining the installation height of the equipment, the shooting distance, and the resolution of different models of equipment and other objective theoretical values ​​to measure and count the effective visible length.

[0101] It can be seen that this optional embodiment can determine all evaluation influence factors corresponding to each high-position video surveillance site, and further determine the calculation method of each evaluation influence factor, which can improve the accuracy of intelligent evaluation of high-position video surveillance sites through evaluation influence factors, thereby improving the accuracy of site selection for high-position video surveillance.

[0102] In another optional embodiment, each evaluation impact factor has a corresponding factor weight parameter;

[0103] And, based on the evaluation impact factor, each high-position video surveillance station site is evaluated to obtain the evaluation result of each high-position video surveillance station site, which may include:

[0104] For each high-position video surveillance station site, an evaluation grade of the high-position video surveillance station site is determined based on each evaluation impact factor and a factor weight parameter of the evaluation impact factor; the evaluation grade is used to evaluate whether the site selection of the high-position video surveillance station site is feasible in the form of a grade;

[0105] For each high-position video surveillance station site, the high-position video surveillance station site is evaluated according to the evaluation level of the high-position video surveillance station site to obtain an evaluation result of the high-position video surveillance station site.

[0106] In this optional embodiment, all evaluation influencing factors include the effective visible length of the road surface, the degree of road surface obstruction, the viewing angle, the visible ratio of road lane lines, the visible ratio of slopes, the visible ratio of bridges and the visible ratio of piers. The corresponding factor weight parameters can be set to {0.2, 0.2, 0.2, 0.1, 0.1, 0.1, 0.1}. If one or some of the evaluation influencing factors do not exist, the factor weight parameters of the remaining evaluation influencing factors can be normalized and calculated.

[0107] It can be seen that this optional embodiment can determine the evaluation level of the high-altitude video surveillance site according to the factor weight parameters corresponding to the evaluation impact factors, thereby improving the accuracy of calculating the evaluation level through the evaluation impact factors; further determine the evaluation results through the evaluation level, thereby improving the accuracy of the evaluation results, and then improving the accuracy of the site selection for high-altitude video surveillance, which is facilitating the construction of high-altitude video surveillance on highways.

[0108] In yet another optional embodiment, determining at least one evaluation influencing factor corresponding to each high-level video surveillance station based on the real-view map information may include:

[0109] For each high-position video surveillance station site, at least one evaluation influencing factor is determined based on the site map information of the high-position video surveillance station site; the site map information is the map information corresponding to the road section in the real-life map information that matches the high-position video surveillance station site.

[0110] It can be seen that this optional embodiment can determine the evaluation influence factor based on the site map information of the high-altitude video surveillance station, thereby improving the accuracy of the process of determining the evaluation influence factor, and can also improve the accuracy of the intelligent evaluation of the high-altitude video surveillance station site through the evaluation influence factor, thereby improving the accuracy of the site selection for high-altitude video surveillance, which is beneficial to the construction of high-altitude video surveillance on highways.

[0111] In another optional embodiment, each evaluation impact factor has a corresponding target level set; the target level set is used to determine the degree of fit between the evaluation impact factor and the high-level video surveillance station site corresponding to the evaluation impact factor; the target level set includes at least two target levels;

[0112] And, for each high-position video surveillance station site, determining the evaluation level of the high-position video surveillance station site based on each evaluation impact factor and the factor weight parameter of the evaluation impact factor may include:

[0113] For each evaluation impact factor corresponding to each high-position video surveillance station site, a level evaluation parameter at each target level of the evaluation impact factor is calculated based on the evaluation impact factor;

[0114] For each high-level video surveillance station, based on the factor weight parameter of each corresponding evaluation impact factor, the level evaluation parameters of all corresponding evaluation impact factors at each target level are weighted summed to obtain the weighted evaluation parameters of the high-level video surveillance station at each target level;

[0115] For each high-position video surveillance site, the weighted evaluation parameters of the high-position video surveillance site at each target level are numerically ranked, and the evaluation level of the high-position video surveillance site is determined according to the target level corresponding to the weighted evaluation parameter with the highest value.

[0116] In this optional embodiment, the target level set is used to determine the degree of fit between the evaluation impact factor and the high-position video surveillance station site corresponding to the evaluation impact factor. Therefore, if there are two target levels, namely the first target level and the second target level, they can be respectively corresponded to the degree (level) of "better" and "worse".

[0117] In this optional embodiment, for each evaluation influence factor corresponding to each high-level video surveillance station site, the level evaluation parameter of the evaluation influence factor at each target level is calculated based on the evaluation influence factor. The specific method may be that for each evaluation influence factor, the evaluation influence factor is respectively substituted into the calculation formula of "better" (first target level) and "poor" (second target level) to calculate the level evaluation parameters of "better" (first target level) and "poor" (second target level); for each high-level video surveillance station site, based on the factor weight parameter of each evaluation influence factor corresponding to it, the level evaluation parameters of all the evaluation influence factors corresponding to it at each target level are weighted summed to obtain the weighted evaluation parameters of the high-level video surveillance station site at each target level. The specific method may be that the "better" (first target level) of each evaluation influence factor of the same station site is weighted summed to obtain the "better" (first target level) For each high-level video surveillance station site, the weighted evaluation parameters of the high-level video surveillance station site at each target level are numerically sorted, and the evaluation level of the high-level video surveillance station site is determined according to the target level corresponding to the weighted evaluation parameter with the highest value. The specific method may be to numerically compare the weighted evaluation parameters of the “better” (first target level) and the weighted evaluation parameters of the “poor” (second target level). When the weighted evaluation parameters of the “better” (first target level) are higher in value, the evaluation level of the high-level video surveillance station site is “better” (first target level), and when the weighted evaluation parameters of the “poor” (second target level) are higher in value, the evaluation level of the high-level video surveillance station site is “poor” (second target level).

[0118] In this optional embodiment, each high-position video surveillance station site and each corresponding evaluation impact factor can be represented in a matrix format.

[0119] It can be seen that this optional embodiment can calculate the level evaluation parameters at each target level of the evaluation impact factor, and then obtain the weighted evaluation parameters of each high-level video surveillance site at each target level, which is beneficial to improving the accuracy of the weighted evaluation parameters obtained by evaluating the impact factor; further, the evaluation level can be determined by the weighted evaluation parameters, which improves the accuracy of the evaluation level, and then improves the accuracy of the intelligent evaluation of the high-level video surveillance site, thereby improving the accuracy of the site selection for high-level video surveillance.

[0120] In yet another optional embodiment, the target level includes a first target level, a second target level, and a third target level;

[0121] Moreover, for each evaluation impact factor corresponding to each high-position video monitoring site, calculating the level evaluation parameters for each target level of the evaluation impact factor may include:

[0122] For each evaluation impact factor of each high-position video monitoring site, calculating the level evaluation parameters for the first target level, the second target level, and the third target level of the evaluation impact factor;

[0123] The formula for the level evaluation parameters of the first target level is as follows:

[0124]

[0125] U A(x) represents the level evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; a < b; the first critical value and the second critical value are determined based on each evaluation impact factor;

[0126] The formula for the level evaluation parameters of the second target level is as follows:

[0127]

[0128] U A(x) represents the level evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; c is the third critical value corresponding to the evaluation impact factor; d is the fourth critical value corresponding to the evaluation impact factor; c < a < b < d; the first critical value, the second critical value, the third critical value, and the fourth critical value are determined based on each evaluation impact factor;

[0129] The formula for the level evaluation parameters of the third target level is as follows:

[0130]

[0131] U A(x) represents the level evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; a < b; the first critical value and the second critical value are determined based on each evaluation impact factor.

[0132] In this optional embodiment, the target level set is used to determine the degree of fit between the evaluation impact factor and the high-position video surveillance station site corresponding to the evaluation impact factor, so the first target level, the second target level and the third target level can be respectively corresponded to the degree (level) of "better", "general" and "poor"; in this optional embodiment, for example, the level of the effective visible length of the road surface exceeding 1000 meters is "better" (first target level), the level within the range of 500 to 1000 meters is "general" (second target level), and the level below 500 meters is "poor" (third target level), and a=500 and b=1000 can be set; for another example, the visible ratio of road lane lines, the level of more than 90% of the lane lines visible is "better" (first target level), the level of more than 60% to 90% of the lane lines visible is "general" (second target level), and the level of less than 60% of the lane lines visible is "poor" (third target level), and a=0.6 and b=0.9 can be set.

[0133] It can be seen that this optional embodiment can improve the accuracy of the calculation of level evaluation parameters by calculating the level evaluation parameters of different target levels, thereby improving the accuracy of the weighted evaluation parameters and the evaluation levels, thereby improving the accuracy of the intelligent evaluation of high-position video surveillance sites.

[0134] In yet another optional embodiment, before obtaining the real-view map information of the target road, the method may further include:

[0135] Collect real images of the target road through drone oblique photography;

[0136] Determine whether there is a near-field blind spot in the real image of the section of the target road that matches the high-position video surveillance station. If the judgment result is yes, perform a blind spot filling operation on the real image of the section of the target road that matches the high-position video surveillance station based on a manual collection method to obtain the target road image data; the near-field blind spot is the near-field area that cannot be collected when the real image is collected by the oblique photography method of the drone; the target road image data includes the real images of the target road collected from multiple angles and the pixel points corresponding to the real images;

[0137] The target road image data is subjected to multi-view stereo matching and the pixel points representing the same position in the target road image data are extracted to perform aerial triangulation to obtain a three-dimensional point cloud corresponding to the target road image data; multi-view stereo matching means matching the real images of the target road collected from multiple angles with each other;

[0138] A three-dimensional space model is generated based on the three-dimensional point cloud, and the real-life map information of the target road is determined from the three-dimensional space model.

[0139] It can be seen that the method described in the embodiment of the present invention can be implemented by combining the oblique photography method of the drone with the manual shooting method, and utilizing methods such as multi-view stereo matching, to obtain the real-life map information of the target road by generating a three-dimensional spatial model, which is beneficial to improving the accuracy of the real-life map information, and further improving the efficiency and accuracy of site selection for high-altitude video surveillance, thereby facilitating the construction of high-altitude video surveillance on highways.

[0140] Embodiment 2

[0141] See also Figure 2 , Figure 2 The present invention discloses a method for evaluating the site selection of a high-level video surveillance system for a highway. Figure 2 The described highway high-position video surveillance site selection evaluation method can be applied to a highway high-position video surveillance site selection evaluation device, and the highway high-position video surveillance site selection evaluation device can be integrated in a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 1 As shown, the highway high-position video surveillance site selection and evaluation method may include the following operations:

[0142] 201. Determine multiple high-position video surveillance station locations to be evaluated on the target road;

[0143] 202. Collect the real image of the target road through the drone oblique photography method;

[0144] In the embodiment of the present invention, during the acquisition process, accurate geographic information can be embedded by combining Beidou satellite positioning technology to collect real images of highways with multi-source information;

[0145] 203. Determine whether there is a near-view blind spot in the real image of the road section matching the high-position video surveillance station in the target road. If the determination result is yes, perform a blind spot filling operation on the real image of the road section matching the high-position video surveillance station based on a manual collection method to obtain the target road image data.

[0146] In the embodiment of the present invention, the near-view blind area is a near-view area that cannot be captured when the drone collects real images in an oblique photography mode; the target road image data includes real images of the target road collected from multiple angles and pixel points corresponding to the real images;

[0147] 204. Perform multi-view stereo matching on the target road image data and extract pixel points representing the same position in the target road image data to perform aerial triangulation to obtain a three-dimensional point cloud corresponding to the target road image data;

[0148] In the embodiment of the present invention, multi-view stereo matching means matching the real images of the target road collected from multiple angles; the multi-view stereo matching and point cloud method can be to use Mirauge3D modeling software to match the collected highway image data and extract the pixels with the same position in the image, perform empty triangulation operation, and generate a high-density three-dimensional point cloud;

[0149] 205. Based on the three-dimensional point cloud, determine a three-dimensional space framework model that matches the three-dimensional point cloud; fuse the three-dimensional point cloud with the three-dimensional space framework model to obtain real-view map information of the target road;

[0150] 206. Acquire real-view map information of the target road;

[0151] 207. Determine at least one evaluation impact factor corresponding to each high-position video surveillance station based on the real-scene map information;

[0152] 208. Based on the evaluation impact factor, each high-position video surveillance station site is evaluated to obtain an evaluation result of each high-position video surveillance station site;

[0153] In the embodiment of the present invention, the evaluation result of each high-position video monitoring station site is used as a basis for site selection.

[0154] It can be seen that the method described in the embodiment of the present invention can obtain real-life map information of the target road by combining the oblique photography method of a drone with the manual photography method, and utilizing methods such as multi-view stereo matching, which is beneficial to improving the accuracy of the real-life map information. Furthermore, based on the real-life map information, multiple evaluation influencing factors of the high-altitude video surveillance site to be evaluated are intelligently analyzed, and corresponding evaluation results are obtained, thereby improving the efficiency and accuracy of site selection for high-altitude video surveillance, which is beneficial to the construction of high-altitude video surveillance on highways.

[0155] Embodiment 3

[0156] See also Figure 3 , Figure 3 The invention discloses a high-level video surveillance site selection and evaluation device for a highway. Figure 3 As shown, the highway high-position video monitoring site selection and evaluation device may include:

[0157] A determination module 301 is used to determine multiple high-position video monitoring station locations to be evaluated on a target road;

[0158] An acquisition module 302 is used to acquire real-view map information of a target road;

[0159] The determination module 301 is further used to determine at least one evaluation impact factor corresponding to each high-level video monitoring station based on the real-scene map information;

[0160] The evaluation module 303 is used to evaluate each high-position video surveillance station site based on the evaluation influencing factors to obtain the evaluation result of each high-position video surveillance station site; the evaluation result of each high-position video surveillance station site is used as a basis for site selection.

[0161] It can be seen that the device described in the embodiment of the present invention can intelligently analyze multiple evaluation influencing factors of the high-altitude video surveillance site to be evaluated based on the real-life map information of the highway, and obtain corresponding evaluation results, thereby improving the efficiency and accuracy of site selection for high-altitude video surveillance, which is beneficial to the construction of high-altitude video surveillance on highways.

[0162] In an optional embodiment, all evaluation influencing factors corresponding to each high-position video surveillance station include at least one of the effective visible length of the road surface, the degree of road surface obstruction, the viewing angle, the visible ratio of road lane lines, the visible ratio of slopes, the visible ratio of bridges and the visible ratio of piers; the effective visible length of the road surface represents the road surface length corresponding to the effective visible road surface that can be identified by the computer program; the degree of road surface obstruction represents the ratio of the area of ​​the effective visible road surface obstructed by objects within the effective visible road surface range to the total area of ​​the target road; the viewing angle represents the angle between the road surface normal of the target road and the main optical axis of the camera of the high-position video surveillance station; the visible ratio of road lane lines represents the ratio of the visible length of road lane lines to the total length of road lane lines within the effective visible road surface range; the visible ratio of slopes represents the ratio of the visible area of ​​slopes to the total area of ​​slopes; the visible ratio of bridges represents the ratio of the visible area of ​​bridges to the total area of ​​bridges; the visible ratio of piers represents the ratio of the visible area of ​​piers to the total area of ​​piers.

[0163] It can be seen that the device described in the embodiment of the present invention can determine all evaluation influence factors corresponding to each high-position video surveillance site, and further determine the calculation method of each evaluation influence factor, which can improve the accuracy of intelligent evaluation of high-position video surveillance sites through evaluation influence factors, thereby improving the accuracy of site selection for high-position video surveillance.

[0164] In another optional embodiment, each evaluation impact factor has a corresponding factor weight parameter;

[0165] Furthermore, the evaluation module 303 evaluates each high-position video surveillance site based on the evaluation impact factor, and the specific method of obtaining the evaluation result of each high-position video surveillance site may include:

[0166] For each high-position video surveillance station site, an evaluation grade of the high-position video surveillance station site is determined based on each evaluation impact factor and a factor weight parameter of the evaluation impact factor; the evaluation grade is used to evaluate whether the site selection of the high-position video surveillance station site is feasible in the form of a grade;

[0167] For each high-position video surveillance station site, the high-position video surveillance station site is evaluated according to the evaluation level of the high-position video surveillance station site to obtain an evaluation result of the high-position video surveillance station site.

[0168] It can be seen that this optional embodiment can determine the evaluation level of the high-altitude video surveillance site according to the factor weight parameters corresponding to the evaluation impact factors, thereby improving the accuracy of calculating the evaluation level through the evaluation impact factors; further determine the evaluation results through the evaluation level, thereby improving the accuracy of the evaluation results, and then improving the accuracy of the site selection for high-altitude video surveillance, which is facilitating the construction of high-altitude video surveillance on highways.

[0169] In another optional embodiment, the specific manner in which the determination module 301 determines at least one evaluation impact factor corresponding to each high-level video surveillance station based on the real-view map information may include:

[0170] For each high-position video surveillance station site, at least one evaluation influencing factor is determined based on the site map information of the high-position video surveillance station site; the site map information is the map information corresponding to the road section in the real-life map information that matches the high-position video surveillance station site.

[0171] It can be seen that the device described in the embodiment of the present invention can determine the evaluation influence factor based on the site map information of the high-level video surveillance site, thereby improving the accuracy of the process of determining the evaluation influence factor, and can improve the accuracy of the intelligent evaluation of the high-level video surveillance site through the evaluation influence factor, thereby improving the accuracy of the site selection for high-level video surveillance, which is facilitating the construction of high-level video surveillance on highways.

[0172] In another optional embodiment, each evaluation impact factor has a corresponding target level set; the target level set is used to determine the degree of fit between the evaluation impact factor and the high-level video surveillance station site corresponding to the evaluation impact factor; the target level set includes at least two target levels;

[0173] Furthermore, the specific manner in which the evaluation module 303 determines the evaluation level of each high-position video surveillance site based on each evaluation impact factor and the factor weight parameter of the evaluation impact factor for each high-position video surveillance site may include:

[0174] For each evaluation impact factor corresponding to each high-position video surveillance station site, a level evaluation parameter at each target level of the evaluation impact factor is calculated based on the evaluation impact factor;

[0175] For each high-position video surveillance site, based on the factor weight parameters of each evaluation impact factor corresponding thereto, the level evaluation parameters of all the evaluation impact factors corresponding thereto at each target level are weighted and summed to obtain the weighted evaluation parameters of the high-position video surveillance site at each target level;

[0176] For each high-position video surveillance site, the weighted evaluation parameters of the high-position video surveillance site at each target level are numerically sorted, and according to the target level corresponding to the highest weighted evaluation parameter, the evaluation level of the high-position video surveillance site is determined.

[0177] It can be seen that implementing the device described in the embodiments of the present invention can calculate the level evaluation parameters of each evaluation impact factor at each target level, and then obtain the weighted evaluation parameters of each high-position video surveillance site at each target level, which is beneficial to improving the accuracy of obtaining the weighted evaluation parameters through the evaluation impact factors; further, the evaluation level can be determined through the weighted evaluation parameters, improving the accuracy of the evaluation level, and further improving the accuracy of the intelligent evaluation of the high-position video surveillance site, thereby improving the accuracy of the site selection for the high-position video surveillance.

[0178] In another alternative embodiment, the target levels include a first target level, a second target level, and a third target level;

[0179] Moreover, the specific manner in which the evaluation module 303 calculates the level evaluation parameters of each evaluation impact factor corresponding to each high-position video surveillance site at each target level based on the evaluation impact factor may include:

[0180] For each evaluation impact factor of each high-position video surveillance site, calculate the level evaluation parameters of the evaluation impact factor at the first target level, the second target level, and the third target level based on the evaluation impact factor;

[0181] The formula for calculating the level evaluation parameter of the first target level is as follows:

[0182]

[0183] U A(x) represents the level evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; a < b; the first critical value and the second critical value are determined based on each evaluation impact factor;

[0184] The formula for calculating the level evaluation parameter of the second target level is as follows:

[0185]

[0186] U A(x) represents the level evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; c is the third critical value corresponding to the evaluation impact factor; d is the fourth critical value corresponding to the evaluation impact factor; c < a < b < d; the first critical value, the second critical value, the third critical value and the fourth critical value are determined based on each evaluation impact factor;

[0187] For the level evaluation parameter of the third target level, the calculation formula is as follows:

[0188]

[0189] U A(x) represents the level evaluation parameter; x represents the evaluation impact factor, a is the first critical value corresponding to the evaluation impact factor; b is the second critical value corresponding to the evaluation impact factor; a < b; the first critical value and the second critical value are determined based on each evaluation impact factor.

[0190] It can be seen that implementing the device described in the embodiments of the present invention can improve the accuracy of calculating the level evaluation parameter by calculating the level evaluation parameters of different target levels, and further improve the accuracy of the weighted evaluation parameter and the evaluation level, thereby improving the accuracy of the intelligent evaluation of the high-position video monitoring site.

[0191] In another alternative embodiment, before the acquisition module 302 acquires the real scene map information of the target road, the device may further include:

[0192] A collection module 304, configured to collect real images of the target road by means of drone oblique photography;

[0193] A judgment module 305, configured to judge whether there is a near-view blind area in the real image of the section of the target road that matches the high-position video monitoring site. When the judgment result is yes, perform a blind area filling operation on the real image of the section of the target road that matches the high-position video monitoring site based on the manual collection method to obtain the target road image data; the near-view blind area is a near-view area that cannot be collected when collecting real images by means of drone oblique photography; the target road image data includes real images of the target road collected from multiple angles and the pixel points corresponding to the real images;

[0194] An operation module 306, configured to perform multi-view stereo matching on the target road image data and extract the pixel points representing the same position in the target road image data to perform aerial triangulation operations to obtain the three-dimensional point cloud corresponding to the target road image data; multi-view stereo matching means mutual matching of real images of the target road collected from multiple angles;

[0195] The fusion module 307 is used to determine a 3D space framework model that matches the 3D point cloud based on the 3D point cloud; and fuse the 3D point cloud with the 3D space framework model to obtain real-view map information of the target road.

[0196] It can be seen that the device described in the embodiment of the present invention can obtain real-life map information of the target road by combining the oblique photography method of a drone with the manual photography method, and utilizing methods such as multi-view stereo matching, which is beneficial to improving the accuracy of the real-life map information. Furthermore, based on the real-life map information, multiple evaluation influencing factors of the high-altitude video surveillance site to be evaluated are intelligently analyzed, and corresponding evaluation results are obtained, thereby improving the efficiency and accuracy of site selection for high-altitude video surveillance, which is beneficial to the construction of high-altitude video surveillance on highways.

[0197] Embodiment 4

[0198] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another highway high-position video monitoring site selection and evaluation device disclosed in an embodiment of the present invention. Figure 5 As shown, the highway high-position video monitoring site selection and evaluation device may include:

[0199] A memory 401 storing executable program codes;

[0200] a processor 402 coupled to the memory 401;

[0201] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the highway high-position video surveillance site selection and evaluation method described in the first embodiment of the present invention.

[0202] Embodiment 5

[0203] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the highway high-position video surveillance site selection and evaluation method described in the first embodiment of the present invention.

[0204] Embodiment 6

[0205] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the highway high-altitude video surveillance site selection and evaluation method described in Example 1.

[0206] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0207] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0208] Finally, it should be noted that the method and device for site selection and evaluation of high-altitude video surveillance of highways disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for evaluating the site selection of high-level video surveillance on a highway, characterized in that: The method comprises: Determine the locations of multiple high-position video surveillance stations on the target road to be evaluated; Acquire real-view map information of the target road; Determine at least one evaluation impact factor corresponding to each of the high-level video surveillance stations based on the real-life map information; Based on the evaluation influencing factors, each of the high-position video surveillance station sites is evaluated to obtain an evaluation result of each of the high-position video surveillance station sites; the evaluation result of each of the high-position video surveillance station sites is used as a basis for site selection.

2. The method for evaluating the site selection of high-level video surveillance for highways according to claim 1, characterized in that: All the evaluation influencing factors corresponding to each of the high-position video monitoring stations include at least one of the effective visible length of the road surface, the degree of road surface obstruction, the viewing angle, the visible ratio of road lane lines, the visible ratio of slopes, the visible ratio of bridges, and the visible ratio of bridge piers; the effective visible length of the road surface represents the length of the road surface corresponding to the effective visible road surface that can be identified by the computer program; The road obstruction degree indicates the ratio of the area of ​​the effective visible road surface obstructed by objects to the total area of ​​the target road within the effective visible road surface range; the viewing angle indicates the angle between the road surface normal of the target road and the main optical axis of the camera of the high-position video monitoring station; the road lane line visible ratio indicates the ratio of the visible length of the road lane line to the total length of the road lane line within the effective visible road surface range; the slope visible ratio indicates the ratio of the visible area of ​​the slope to the total area of ​​the slope; the bridge visible ratio indicates the ratio of the visible area of ​​the bridge to the total area of ​​the bridge; the pier visible ratio indicates the ratio of the visible area of ​​the pier to the total area of ​​the pier.

3. The method for evaluating the site selection of high-level video surveillance for expressways according to claim 1 or 2, characterized in that: Each of the evaluation impact factors has a corresponding factor weight parameter; And, based on the evaluation influencing factor, each of the high-position video surveillance sites is evaluated to obtain an evaluation result of each of the high-position video surveillance sites, including: For each of the high-position video surveillance station sites, determining an evaluation grade of the high-position video surveillance station site based on each of the evaluation impact factors and the factor weight parameter of the evaluation impact factor; the evaluation grade is used to evaluate whether the site selection of the high-position video surveillance station site is feasible in the form of a grade; For each of the high-position video surveillance sites, the high-position video surveillance site is evaluated according to the evaluation level of the high-position video surveillance site to obtain an evaluation result of the high-position video surveillance site.

4. The method for evaluating the site selection of high-level video surveillance for expressways according to claim 1, characterized in that: The determining, based on the real-life map information, at least one evaluation impact factor corresponding to each of the high-level video surveillance station locations comprises: For each of the high-position video surveillance sites, at least one evaluation influencing factor is determined based on the site map information of the high-position video surveillance site; the site map information is the map information corresponding to the road section in the real-life map information that matches the high-position video surveillance site.

5. The method for evaluating the site selection of high-level video surveillance for expressways according to claim 3, characterized in that: Each of the evaluation impact factors has a corresponding target level set; the target level set is used to determine the degree of fit between the evaluation impact factor and the high-level video surveillance station site corresponding to the evaluation impact factor; the target level set includes at least two target levels; And, for each of the high-position video surveillance sites, determining the evaluation level of the high-position video surveillance site based on each of the evaluation impact factors and the factor weight parameter of the evaluation impact factor includes: For each of the evaluation impact factors corresponding to each of the high-position video surveillance sites, calculating, based on the evaluation impact factor, a level evaluation parameter at each of the target levels of the evaluation impact factor; For each of the high-level video surveillance sites, based on the factor weight parameter of each of the corresponding evaluation impact factors, weighted sum of the level evaluation parameters of all the corresponding evaluation impact factors at each of the target levels is performed to obtain the weighted evaluation parameters of the high-level video surveillance site at each of the target levels; For each of the high-level video surveillance sites, the weighted evaluation parameters of the high-level video surveillance site at each of the target levels are numerically sorted, and the evaluation level of the high-level video surveillance site is determined based on the target level corresponding to the weighted evaluation parameter with the highest value.

6. The method for evaluating the site selection of high-level video surveillance on a highway according to claim 5, characterized in that: The target levels include a first target level, a second target level and a third target level; And, for each of the evaluation impact factors corresponding to each of the high-position video surveillance sites, calculating the level evaluation parameter of each of the target levels of the evaluation impact factor based on the evaluation impact factor, including: For each of the evaluation impact factors of each of the high-position video surveillance sites, calculating the level evaluation parameters of the first target level, the second target level, and the third target level of the evaluation impact factor based on the evaluation impact factor; The calculation formula of the level evaluation parameter of the first target level is as follows: U A(x) represents the grade evaluation parameter; x represents the evaluation influence factor, a is the first critical value corresponding to the evaluation influence factor; b is the second critical value corresponding to the evaluation influence factor; a < b; the first critical value and the second critical value are determined based on each evaluation influence factor; The calculation formula of the level evaluation parameter of the second target level is as follows: U A(x) represents the grade evaluation parameter; x represents the evaluation influence factor, a is the first critical value corresponding to the evaluation influence factor; b is the second critical value corresponding to the evaluation influence factor; c is the third critical value corresponding to the evaluation influence factor; d is the fourth critical value corresponding to the evaluation influence factor; c < a < b < d; the first critical value, the second critical value, the third critical value and the fourth critical value are determined based on each evaluation influence factor; The calculation formula of the level evaluation parameter of the third target level is as follows: U A(x) represents the grade evaluation parameter; x represents the evaluation influence factor, a is the first critical value corresponding to the evaluation influence factor; b is the second critical value corresponding to the evaluation influence factor; a < b; the first critical value and the second critical value are determined based on each evaluation influence factor.

7. The method for evaluating the site selection of high-level video surveillance for expressways according to claim 1, characterized in that: Before acquiring the real-view map information of the target road, the method further includes: Collecting real images of the target road by using drone oblique photography; Determine whether there is a near-field blind spot in the real image of the road section in the target road that matches the high-position video surveillance station address. When the judgment result is yes, perform a blind spot filling operation on the real image of the road section that matches the high-position video surveillance station address based on a manual collection method to obtain target road image data; the near-field blind spot is a near-field area that cannot be collected when the real image is collected by the drone in an oblique photography method; the target road image data includes real images of the target road collected from multiple angles and pixel points corresponding to the real images; The target road image data is subjected to multi-view stereo matching and pixel points representing the same position in the target road image data are extracted to perform aerial triangulation to obtain a three-dimensional point cloud corresponding to the target road image data; the multi-view stereo matching means matching the real images of the target road collected from multiple angles with each other; Based on the three-dimensional point cloud, a three-dimensional space framework model matching the three-dimensional point cloud is determined; the three-dimensional point cloud is fused with the three-dimensional space framework model to obtain real-view map information of the target road.

8. A high-level video surveillance site selection and evaluation device for a highway, characterized in that: The device comprises: A determination module, used to determine multiple high-position video surveillance station locations to be evaluated on a target road; An acquisition module, used to acquire real-view map information of the target road; The determination module is further used to determine at least one evaluation impact factor corresponding to each of the high-position video surveillance stations based on the real-scene map information; An evaluation module is used to evaluate each of the high-position video surveillance sites based on the evaluation influencing factors to obtain an evaluation result of each of the high-position video surveillance sites; the evaluation result of each of the high-position video surveillance sites is used as a basis for site selection.

9. A high-level video surveillance site selection and evaluation device for a highway, characterized in that: The device includes: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the highway high-position video surveillance site selection and evaluation method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the highway high-position video surveillance site selection and evaluation method as described in any one of claims 1-7.

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