Road visibility processing method, system and storage medium based on image analysis

Abstract

The present invention discloses a road visibility processing method, system and storage medium based on image analysis, which relates to the field of road image monitoring technology. The method includes obtaining the position information of at least two imaging acquisition points on the monitored road section based on traffic facility design information, and using the imaging acquisition points to collect road condition image information of the monitored road section in real time. The present invention uses the imaging acquisition points to collect the activity trajectory of the monitored target on the road in real time, and dynamically captures the direction of the monitored target in real time based on the activity trajectory feedback of the monitored target, and then can deduce whether the monitored target has an accident. At the same time, the accident level of the monitored target can also be derived based on the real-time road condition image. That is, it effectively solves the shortcomings of the existing technology that the accident situation cannot be understood in a timely manner due to low road traffic visibility.

Description

Technical Field

[0001] The present invention relates to the technical field of road image monitoring, and in particular to a road visibility processing method, system and storage medium based on image analysis. Background Art

[0002] Low-visibility meteorological disasters are disasters to road traffic facilities. Their main cause is heavy fog or foggy patches, which will seriously affect vehicles traveling on the road and pedestrians passing by. They greatly affect the driver's vision in road traffic, increase the risk of traffic accidents, and pose a serious threat to road safety. Not only will it make it difficult for the driver to see the road ahead and traffic signs, it may also increase the braking distance of the vehicle, making it difficult to respond in time.

[0003] On the one hand, low visibility weather greatly increases the rate of road traffic accidents. On the other hand, low visibility makes it impossible to understand the accident situation in time after a road traffic accident occurs, which in turn affects rescue. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art that the accident situation cannot be understood in time due to low road traffic visibility, and to propose a road visibility processing method, system and storage medium based on image analysis.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for processing road visibility based on image analysis, comprising:

[0007] According to the traffic facility design information, the location information of at least two imaging collection points on the monitored road section is obtained, and the road condition image information of the monitored road section is collected in real time using the imaging collection points;

[0008] The road condition image information of the monitored road section is collected in real time according to the imaging collection points, the visibility level of the monitored road section is obtained, and the activity trajectory of the monitored target is combined to determine the accident occurrence signal;

[0009] According to the accident signal and the imaging collection points, the road condition image information of the monitored section is collected in real time, and the accident section interval is pre-determined in combination with the location information of the imaging collection points on the monitored section;

[0010] Determine the location information of at least one accident imaging collection point on the accident section based on the predetermined accident section and in combination with traffic facility design information, and use the accident imaging collection point to collect a road condition image of the accident section in real time;

[0011] The accident status of the monitored target is determined based on the road condition image of the accident section and the activity trajectory of the monitored target.

[0012] In some optional solutions, the method for obtaining the visibility level of the monitored road section includes:

[0013] The imaging acquisition points collect road condition image information of the monitored road section in real time, perform image recognition analysis, and obtain the road condition image feature distribution information of the monitored road section;

[0014] Based on the road condition image feature distribution information of the monitored road section and in combination with the position information of the imaging acquisition points, the road condition image information of the monitored road section is divided into image regions to obtain road condition image feature distribution information of at least two sub-regions in the target road section;

[0015] According to the traffic image feature distribution information of the target road section, the traffic image feature distribution information of at least two sub-areas in the target road section is compared to obtain image feature difference information of each sub-area in the target road section;

[0016] The visibility level of the monitored road section is determined based on the image feature difference information of each sub-area in the target road section.

[0017] In some optional solutions, the road condition image feature distribution information of the monitored road section includes:

[0018] Image grayscale value.

[0019] In some optional solutions, the method for determining the accident occurrence signal includes:

[0020] According to the design information of traffic facilities and the location information of imaging acquisition points, combined with the activity trajectory of the monitored target, the pre-activity dynamic information of the monitored target is determined;

[0021] According to the pre-activity dynamic information of the monitored target, the location information of the imaging acquisition point is determined, and the imaging acquisition point is used to collect the current road condition image information in real time;

[0022] According to the pre-activity dynamic information of the monitored target, the road condition image features of the monitored section are identified and analyzed to obtain abnormal signals of the monitored section;

[0023] The accident occurrence signal is determined based on the abnormal signal of the monitored road section and the pre-activity dynamic information of the monitored target.

[0024] In some optional solutions, the pre-activity dynamic information of the monitored target includes:

[0025] A combination of one or more of the speed and direction of movement of the monitored target.

[0026] In some optional solutions, the method for determining the accident status of the monitored target includes:

[0027] According to the position information of the imaging acquisition point, a point is selected in the road condition image of the accident section as the first reference point;

[0028] Constructing a position division coordinate system in the traffic image of the target road section using the first reference point; obtaining position coordinates of each area in the traffic image of the target road section based on the position division coordinate system;

[0029] Extracting grayscale features from the road condition image of the target road section to determine the image feature information of the reference object in the target road section;

[0030] Based on the position division coordinate system and in combination with the image feature information of the reference object in the target road section, the road condition image of the target road section is divided into multiple regions along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system, at least two section distribution maps are obtained in the road condition image of the target road section, and grayscale value information in each section distribution map is determined in combination with the image feature information of the reference object in the target road section;

[0031] According to the gray value information in the distribution map of each section and combined with the activity trajectory of the monitored target, the accident status of the monitored target is determined.

[0032] In a second aspect, the present invention provides a road visibility processing system based on image analysis, which adopts any one of the road visibility processing methods based on image analysis described in the first aspect.

[0033] In some optional solutions, the processing system further includes:

[0034] An image acquisition module, which is arranged at an imaging point and is used to acquire road condition image information in real time;

[0035] An image processing module, configured to process the road condition image information;

[0036] An information recognition module, which is used to determine whether there is an abnormality in the monitored road section;

[0037] The early warning feedback module is used to feedback the accident status.

[0038] In a third aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, it implements a road visibility processing method based on image analysis as described in any one of the first aspects.

[0039] The beneficial effects of the present invention are:

[0040] The present invention uses imaging acquisition points to capture the movement trajectory of monitored targets on the road in real time. Based on the feedback from the monitored targets' movement trajectory, the present invention dynamically captures the targets' movements in real time, thereby deriving whether the monitored targets have been involved in an accident. Furthermore, the accident level of the monitored targets can be derived based on the real-time road condition images. This effectively addresses the drawback of existing technologies that prevents timely monitoring of accident situations due to low road traffic visibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the overall process of a road visibility processing method based on image analysis provided in an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a partial flow chart of a road visibility processing method based on image analysis provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a position division coordinate system division of a road visibility processing method based on image analysis provided in an embodiment of the present invention;

[0044] Figure 4 Schematic diagram of the contour simulation reference line division of a road visibility processing method based on image analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0047] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0048] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0049] Example

[0050] Reference Figures 1 to 4 In order to solve the shortcomings of the prior art that the accident situation cannot be timely understood due to low road traffic visibility, the present invention provides, in this embodiment, a road visibility processing method based on image analysis in the first aspect. The processing method uses imaging acquisition points to collect the activity trajectory of the monitored target on the road in real time, and dynamically captures the direction of the monitored target in real time based on the activity trajectory feedback of the monitored target, and then can deduce whether the monitored target has an accident. At the same time, the accident level of the monitored target can also be derived based on the real-time road condition image. That is, it effectively solves the shortcomings of the prior art that the accident situation cannot be timely understood due to low road traffic visibility.

[0051] Specifically, the processing method includes: obtaining location information of at least two imaging collection points on a monitored road section based on traffic facility design information, and using the imaging collection points to collect road condition image information of the monitored road section in real time. Specifically, after an accident occurs or during a warning drill, the monitored road section area is pre-determined, and then, based on the traffic route planning for the monitored road section area, at least two imaging collection points and their locations are determined, and road condition image information is collected using the imaging collection points. This provides a reference for further determining the section where the accident occurred. The imaging collection points then collect road condition image information of the monitored road section in real time to obtain the visibility level of the monitored road section, i.e., determine whether heavy fog or other low visibility conditions exist at the current location of the monitored road section area. Furthermore, the method combines the movement trajectory of the monitored target to determine whether the monitored target has experienced an accident on the road section based on the monitored target's movement trajectory and the visibility conditions in the monitored road section area. This allows for further screening of road conditions within the monitored road section area. That is, if an accident occurs on the monitored road section, road condition image information of the monitored road section can be collected in real time based on the accident signal and the imaging acquisition points. Combined with the location information of the imaging acquisition points on the monitored road section, the accident section interval can be pre-determined to confirm that an accident has occurred on the monitored road section. Then, based on the pre-determined accident section interval and in combination with the traffic facility design information, the location information of at least one accident imaging acquisition point within the accident section interval can be determined. Real-time road condition images of the accident section interval can be collected using the accident imaging acquisition points. Based on the road condition images of the accident section interval and the movement trajectory of the monitored target, the accident status of the monitored target can be determined. In this embodiment, the location information of the monitored road section area can be determined on a large scale based on the imaging points in the traffic facility design information. Then, based on the movement trajectory of the monitored target, the accident location can be searched within the monitored road section area. Specifically, the location of the accident area of ​​the monitored target can be determined based on whether the monitored target appears within the monitored road section area. The road condition image can then be captured using imaging acquisition points near this location to determine the accident status. Specifically, by using imaging points to capture the movement trajectory of monitored targets on the road in real time, the system dynamically captures the target's direction based on the trajectory feedback, and can then deduce whether the target has been involved in an accident. Furthermore, the level of the accident can be inferred based on the real-time road condition image. This effectively addresses the shortcomings of existing technologies that hinder timely understanding of accident situations due to low road visibility.

[0052] In this embodiment, in order to facilitate understanding of how to analyze the visibility level of the monitored road section, the following description is provided. Specifically, the method for obtaining the visibility level of the monitored road section includes:

[0053] The imaging acquisition point collects the road condition image information of the monitored section in real time and performs image recognition analysis to obtain the road condition image feature distribution information of the monitored section; that is, the grayscale value of a certain fixed feature in the road condition image of the monitored section can be extracted to determine the road condition image feature distribution information in the road condition image of the monitored section; then, based on the road condition image feature distribution information of the monitored section and combined with the position information of the imaging acquisition point, the road condition image information of the monitored section is divided into image areas to obtain the road condition image feature distribution information of at least two sub-areas in the target section; that is, when it is necessary to determine the visibility level, the grayscale value of a certain fixed feature in the road condition image of the monitored section can be obtained in advance, and then based on the pre-acquired The grayscale value of a certain fixed feature in the road condition image of the monitored section can be used to divide the road condition image collected in real time into image areas according to the actual distance to determine the road condition division image of the target section (sub-region road condition image feature distribution information). Then, based on the road condition image feature distribution information of the target section, the road condition image feature distribution information of at least two sub-regions in the target section is compared to obtain the image feature difference information of each sub-region in the target section; that is, the sub-region road condition image feature distribution information is respectively compared with the grayscale value of a certain fixed feature in the road condition image of the monitored section obtained in advance to determine the grayscale value difference between the two. Then, based on the image feature difference information of each sub-region in the target section, the visibility level of the monitored section is determined.

[0054] In this embodiment, to facilitate understanding of how to determine an accident occurrence signal, the following description is provided. Specifically, the method for determining an accident occurrence signal includes:

[0055] Based on the design information of traffic facilities and the location information of imaging acquisition points, and in combination with the activity trajectory of the monitored target, the pre-activity dynamic information of the monitored target is determined; that is, based on the driving trajectory and driving dynamic data (such as movement speed and movement direction) of the monitored target, the pre-passing points of the monitored target on the monitored section can be determined. Then, based on the pre-activity dynamic information of the monitored target, the location information of the imaging acquisition points is determined, and the current road condition image information is collected in real time using the imaging acquisition points. Based on the pre-passing points of the monitored target on the monitored section, when the monitored target has not passed the next pre-passing point, the location of the imaging acquisition points of the section where the accident may occur is determined, and the imaging acquisition points of the target section are used to collect road condition images. That is, based on the pre-activity dynamic information of the monitored target, the road condition image features of the monitored section are identified and analyzed to obtain the abnormal signal of the monitored section; then, based on the abnormal signal of the monitored section and the pre-activity dynamic information of the monitored target, the accident occurrence signal is determined. In this embodiment, when the monitored target does not pass the next expected passing point or the required passing point for a long time, the road section monitoring of the monitored target can be started. That is, the search range can be gradually narrowed from the starting position to the end position of the monitored target, and from the previous passing point to the next expected passing point, and finally the location of the road section where the accident occurred of the monitored target is determined. Then, based on the traffic facility design information, the nearest imaging acquisition point in the road section where the accident occurred of the monitored target is retrieved, and real-time road condition image acquisition is performed to determine the location and area where the accident occurred of the monitored target. That is, the pre-activity dynamic information of the monitored target includes: one or more combinations of the movement speed and movement direction of the monitored target.

[0056] In this embodiment, to facilitate understanding of how to determine and analyze the accident status of the monitored target, the following description is provided. Specifically, the method for determining the accident status of the monitored target includes:

[0057] According to the position information of the imaging acquisition point, a point can be selected in advance in the road condition image of the accident section as the first reference point; then a position division coordinate system is constructed in the road condition image of the target section with the first reference point; according to the position division coordinate system, the position coordinates of each area in the road condition image of the target section are obtained; then, the image gray value feature extraction is performed on the road condition image of the target section to determine the image feature information of the reference object in the target section; that is, the road condition image feature extraction is performed on multiple imaging acquisition points in advance, and a point is selected in the road condition image of multiple imaging acquisition points. The grayscale value of the reference object is fixed. Based on a position-dividing coordinate system and combined with the image feature information of the reference object in the target road section, the road condition image of the target road section is divided into multiple regions along the longitudinal or transverse coordinate directions of the position-dividing coordinate system. At least two segment distribution maps are obtained from the road condition image of the target road section. The grayscale value information in each segment distribution map is determined based on the image feature information of the reference object in the target road section. The accident status of the monitored target is then determined based on the grayscale value information in each segment distribution map and the activity trajectory of the monitored target. Specifically, a reference object is pre-selected from the road condition image at each imaging acquisition point in the monitored road section. Image analysis is then performed on the grayscale value features of the reference object. When the monitored target appears in the target road section and an accident occurs, the grayscale value features of the reference object are compared with the pre-set reference object to determine the status of the monitored target in that area, including whether any parts of the monitored target are scattered or disintegrated, and then the accident status characteristics of the monitored target are determined. It should be noted that the reference object can be a road surface, a flower bed, a guardrail, etc.

[0058] In this embodiment, to facilitate more rapid and accurate determination of the location of a monitored target on a traffic road in a monitored section, the processing method further includes: obtaining location information of all imaging acquisition points on the monitored section based on traffic facility design layout information; that is, determining location information such as the geographical distance relationship between all imaging acquisition points on the monitored section based on the traffic facility design route, thereby providing an effective basis for determining the location of the monitored target. Specifically, based on the location information of all imaging acquisition points on the monitored section, the imaging areas of the imaging acquisition points are integrated to obtain at least two sections on the monitored section; then, based on the at least two sections obtained, regional feature information of the monitored section collected in real time by all imaging acquisition points is identified to obtain real-time road condition feature information for each section; that is, image grayscale value feature extraction is performed on each of the divided sections, and then, based on an abnormal road condition warning model and in combination with the real-time road condition feature information of each section, the monitored section is extracted to obtain abnormal sections of the monitored section, thereby determining the location of the accident on the monitored section. Specifically, in order to facilitate understanding of the above-described method of determining the location of an accident in a monitored road section, the following example is provided:

[0059] Assume that there are several imaging acquisition points on the monitored road section, and the imaging acquisition points are distributed in sequence. The coordinate values ​​of the imaging acquisition points are A1(x1, y1, z1), A2(x2, y2, z2), A3(x3, y3, z3), ..., A n (x n ,y n , z n ), since each imaging acquisition point can be considered to be on the same distribution surface according to the design line, the z-axis coordinate value of each imaging acquisition point can be ignored. Therefore, the coordinate values ​​of several imaging acquisition points are A1(x1, y1), A2(x2, y2), A3(x3, y3), ..., A n (x n ,y n ). From this we can see that if n There is A a To A b The interval section contains the accident location in the monitored road section. After combining the above-mentioned image gray value feature extraction of the accident status of the monitored target and the coordinates of each imaging acquisition point, the location can be quickly marked and positioned directly through the coordinate system.

[0060] In this embodiment, since the actual viewing distance range of the imaging acquisition point is wide, in order to quickly locate the position distance of the monitored target in the road condition image of the imaging acquisition point, the processing method also includes: obtaining the nearest imaging acquisition point in the accident-occurring section of the monitored target, and using the imaging acquisition point to perform real-time road condition image acquisition to obtain the accident-occurring area image of the monitored target, and selecting a point in the accident-occurring area image of the monitored target as the first reference point; and then constructing a position division coordinate system in the road condition image of the accident-occurring area image of the monitored target with the first reference point; according to the position division coordinate system, obtaining the monitored target The position coordinates of each area in the accident area image; extracting the image grayscale value features of the accident area image of the monitored target, and simultaneously matching and determining the reference object image feature information corresponding to the accident area image of the monitored target; based on the position division coordinate system and combined with the reference object image feature information in the monitored road section, the accident area image of the monitored target can be divided into multiple areas along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system, obtaining at least two segment distribution maps in the accident area image of the monitored target, and combining the reference object image feature information in the monitored road section to respectively determine the grayscale value information in each segment distribution map. That is, based on the reference object grayscale value corresponding to the monitored target in the accident area image of the monitored target, the actual distance position of the monitored target can be determined.

[0061] Reference Figure 3 and Figure 4 In order to more accurately determine the actual distance position of the monitored target, the first segment dividing line and the second segment dividing line are set in parallel along the longitudinal coordinate direction of the position dividing coordinate system, and the accident area image of the monitored target is segmented into three sections. The distance between the first reference point and the first segment dividing line is L1 meters, the distance between the first reference point and the second segment dividing line is L2 meters, and the second segment dividing line is L3 meters. Assume that the contour outer line M of the monitored target is between the first segment dividing line and the second segment dividing line. At this time, a contour simulation reference line N can be made along the contour outer line M of the monitored target, and the contour simulation reference line N is used as the position line of the monitored target entering between the first segment dividing line and the second segment dividing line, wherein the contour simulation reference line N is parallel to the first segment dividing line and the second segment dividing line. At this time, the distance between the contour simulation reference line N and the first reference point is the visibility distance L n At this time, the distance between the contour simulation reference line N and the first reference point can be calculated by the following method:

[0062] Assume that the coordinate value of the first reference point is A(x1, y1) and the imaging area of ​​the imaging acquisition point is divided equally. Therefore, it can be known that the point B(x1-a, y1) located on one side of the first reference point, that is, the first reference point divides the image of the accident area of ​​the monitored target equally on the vertical coordinate. The coordinate system is divided according to the area size and position of the image of the accident area of ​​the monitored target, and the contour simulation reference line N is parallel to the first segment dividing line and the second segment dividing line. It can be known that the coordinates of the contour simulation reference line N at both ends of the image of the accident area of ​​the monitored target are C(x1-a, y1+n). At this time, according to

[0063]

[0064] in, |AB| is the straight-line distance from point A to point B; |AC| is the straight-line distance from point A to point C; |BC| is the straight-line distance from point B to point C. That is, in this embodiment, at this time:

[0065]

[0066] On the other hand, since the imaging area of ​​the accident area image of the monitored target is a rectangle, it can be known from the calculation method of the rectangle that:

[0067]

[0068] At this time, since the imaging area size of the accident area image of the monitored target is known, and the first reference point bisects the imaging area of ​​the imaging acquisition point, the value of a is known. Therefore, the value of n can be determined by combining Equation 1 and Equation 2, and the distance between the fog simulation reference line N and the first reference point can be determined, thereby judging the visibility distance.

[0069] In a preferred embodiment, in order to reduce the image processing pressure on the road condition image information of the monitored road section, the processing method also includes a method for determining the regional feature information of the monitored road section. This method uses a regional analysis of the changes in image features in the road condition image information of the monitored road section caused by fixed reference objects (such as road guardrails, plants on both sides or in the middle of the road, etc.) or moving reference objects (such as vehicles on the road) in the traffic road facilities in the road condition image information of the monitored road section, thereby reducing unnecessary image feature recognition and reducing the image processing pressure of the road condition image information of the monitored road section. Specifically, the method further includes: performing image recognition analysis on the road condition image information of the monitored road section collected in real time by the imaging acquisition points to obtain grayscale value distribution information of the road condition image of the monitored road section; then dividing the road condition image information of the monitored road section into image regions based on the grayscale value distribution information of the road condition image of the monitored road section and in combination with the position information of the imaging acquisition points to obtain grayscale value distribution information of the road condition image of at least two sub-regions in the monitored road section; that is, dividing the road condition image information of the monitored road section into regions based on the position information of the imaging acquisition points and the grayscale value distribution information of the road condition image of the monitored road section. In other words, dividing the fixed reference objects or moving reference objects in the same region of the road condition image information of the monitored road section into regions based on the coordinate system of the imaging acquisition points and the grayscale value of the image features to determine the regional positions of different reference objects in the road condition image information of the monitored road section. That is, according to the grayscale value distribution information of the road condition image of the monitored section, the grayscale value distribution information of the road condition image of at least two sub-areas in the monitored section is compared, and the grayscale difference information of the images of each sub-area in the monitored section is obtained.

[0070] In a second aspect, the present invention provides a road visibility processing system based on image analysis, which utilizes the road visibility processing method based on image analysis described in any one of the first aspects. In this embodiment, the processing system further includes: an image acquisition module, located at an imaging point, for acquiring road condition image information in real time; an image processing module, for performing image processing on the road condition image information; an information recognition module, for determining whether an abnormality exists in the monitored road section; and an early warning feedback module, for providing feedback on accident conditions. The processing system utilizes imaging acquisition points to capture the movement trajectory of monitored targets on the road in real time. Based on the feedback from the movement trajectory of the monitored targets, the system dynamically captures the direction of the monitored targets in real time, thereby deriving whether an accident has occurred with the monitored targets. Furthermore, the accident level of the monitored targets can be derived based on the real-time road condition images. This effectively addresses the drawback of existing technologies that prevents timely understanding of accident conditions due to low road traffic visibility.

[0071] In some embodiments, the processing system can communicate using any currently known or later developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can interconnect with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or later developed network.

[0072] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0073] The third aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, a road visibility processing method based on image analysis as described in any one of the first aspects is implemented. The computer-readable medium in this embodiment can be written in one or more programming languages ​​or a combination thereof to write computer program codes for performing the operations of some embodiments of the present disclosure, and the above-mentioned programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, C++, and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0074] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0075] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including program code for executing the methods shown in the flowcharts.

[0076] The fourth aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement a road visibility processing method based on image analysis as described in the first aspect. The computer-readable medium may be included in the electronic device; or it may exist independently, that is, not assembled into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can implement a road visibility processing method based on image analysis as described in the first aspect.

[0077] A fifth aspect of the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements a road visibility processing method based on image analysis as described in the first aspect.

[0078] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A road visibility processing method based on image analysis, characterized in that: include: According to the traffic facility design information, the location information of at least two imaging collection points on the monitored road section is obtained, and the road condition image information of the monitored road section is collected in real time using the imaging collection points; The road condition image information of the monitored road section is collected in real time according to the imaging collection points, the visibility level of the monitored road section is obtained, and the activity trajectory of the monitored target is combined to determine the accident occurrence signal; According to the accident signal and the imaging collection points, the road condition image information of the monitored section is collected in real time, and the accident section interval is pre-determined in combination with the location information of the imaging collection points on the monitored section; Determine the location information of at least one accident imaging collection point on the accident section based on the predetermined accident section and in combination with traffic facility design information, and use the accident imaging collection point to collect a road condition image of the accident section in real time; The accident status of the monitored target is determined based on the road condition image of the accident section and the activity trajectory of the monitored target.

2. The method for processing road visibility based on image analysis according to claim 1, characterized in that: The method for obtaining the visibility level of the monitored road section includes: The imaging acquisition points collect road condition image information of the monitored road section in real time, perform image recognition analysis, and obtain the road condition image feature distribution information of the monitored road section; Based on the road condition image feature distribution information of the monitored road section and in combination with the position information of the imaging acquisition points, the road condition image information of the monitored road section is divided into image regions to obtain road condition image feature distribution information of at least two sub-regions in the target road section; According to the traffic image feature distribution information of the target road section, the traffic image feature distribution information of at least two sub-areas in the target road section is compared to obtain image feature difference information of each sub-area in the target road section; The visibility level of the monitored road section is determined based on the image feature difference information of each sub-area in the target road section.

3. The method for processing road visibility based on image analysis according to claim 2, characterized in that: The road condition image feature distribution information of the monitored road section includes: Image grayscale value.

4. The method for processing road visibility based on image analysis according to claim 3, characterized in that: The method for determining an accident occurrence signal comprises: According to the design information of traffic facilities and the location information of imaging acquisition points, combined with the activity trajectory of the monitored target, the pre-activity dynamic information of the monitored target is determined; According to the pre-activity dynamic information of the monitored target, the location information of the imaging acquisition point is determined, and the imaging acquisition point is used to collect the current road condition image information in real time; According to the pre-activity dynamic information of the monitored target, the road condition image features of the monitored section are identified and analyzed to obtain abnormal signals of the monitored section; The accident occurrence signal is determined based on the abnormal signal of the monitored road section and the pre-activity dynamic information of the monitored target.

5. The method for processing road visibility based on image analysis according to claim 4, characterized in that: The pre-activity dynamic information of the monitored target includes: A combination of one or more of the speed and direction of movement of the monitored target.

6. The method for processing road visibility based on image analysis according to claim 5, characterized in that: The method for determining the accident status of the monitored target includes: According to the position information of the imaging acquisition point, a point is selected in the road condition image of the accident section as the first reference point; Constructing a position division coordinate system in the traffic image of the target road section using the first reference point; obtaining position coordinates of each area in the traffic image of the target road section based on the position division coordinate system; Extracting grayscale features from the road condition image of the target road section to determine the image feature information of the reference object in the target road section; Based on the position division coordinate system and in combination with the image feature information of the reference object in the target road section, the road condition image of the target road section is divided into multiple regions along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system, at least two section distribution maps are obtained in the road condition image of the target road section, and grayscale value information in each section distribution map is determined in combination with the image feature information of the reference object in the target road section; According to the gray value information in the distribution map of each section and combined with the activity trajectory of the monitored target, the accident status of the monitored target is determined.

7. A road visibility processing system based on image analysis, characterized in that: A road visibility processing method based on image analysis as described in any one of claims 1 to 6 is adopted.

8. The road visibility processing system based on image analysis according to claim 7, characterized in that: The processing system further comprises: An image acquisition module, which is arranged at an imaging point and is used to acquire road condition image information in real time; An image processing module, configured to process the road condition image information; An information recognition module, which is used to determine whether there is an abnormality in the monitored road section; The early warning feedback module is used to feedback the accident status.

9. A computer-readable medium having a computer program stored thereon, characterized in that: in, When the program is executed by a processor, a road visibility processing method based on image analysis as described in any one of claims 1 to 6 is implemented.

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