CN116978241B - Urban vehicle monitoring method and system based on law enforcement recorder - Google Patents
Urban vehicle monitoring method and system based on law enforcement recorder Download PDFInfo
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- CN116978241B CN116978241B CN202311219917.6A CN202311219917A CN116978241B CN 116978241 B CN116978241 B CN 116978241B CN 202311219917 A CN202311219917 A CN 202311219917A CN 116978241 B CN116978241 B CN 116978241B
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- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 230000000007 visual effect Effects 0.000 claims description 35
- 238000001514 detection method Methods 0.000 claims description 31
- 238000012545 processing Methods 0.000 claims description 31
- 230000005540 biological transmission Effects 0.000 claims description 22
- 238000012937 correction Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000000903 blocking effect Effects 0.000 claims description 8
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/61—Control of cameras or camera modules based on recognised objects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/14—Picture signal circuitry for video frequency region
- H04N5/147—Scene change detection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
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Abstract
The application relates to a city vehicle monitoring method and system based on a law enforcement recorder, and relates to the field of traffic monitoring technology; determining a front image of the vehicle and a corresponding license plate number of the vehicle in the illegal vehicle image; judging whether at least two front images of the vehicle exist under the same license plate number of the vehicle; if yes, determining the type of the illegal vehicle in the front image of the vehicle, and determining a front shooting point according to the type of the illegal vehicle and the front image of the vehicle; determining an optimal shooting point corresponding to the type of the illegal vehicle according to the point matching relation; and determining a shooting deviation value according to the front shooting point and the optimal shooting point, determining the shooting deviation value with the smallest value according to the sorting rule, and deleting the front images of the vehicles except for the front images of the vehicles corresponding to the shooting deviation value under the same license plate number of the vehicles. The method and the device have the effect of reducing the occurrence of the situation that the temporary storage space is fully loaded and the environment image cannot be acquired.
Description
Technical Field
The application relates to the field of traffic monitoring technology, in particular to a city vehicle monitoring method and system based on a law enforcement recorder.
Background
The law enforcement recorder is technical equipment which is worn by police and integrates functions of real-time video and audio shooting, photographing, video recording and the like, integrates functions of shooting, photographing, intercom, positioning and storing, can digitally record dynamic and static site conditions in the law enforcement process through 4G wireless real-time video transmission, and is convenient for police to law enforcement in various environments.
In the related technology, when traffic police processes the illegal parking vehicles on the roadside, the traffic police photographs and records the vehicle conditions through a law enforcement recorder, and then the traffic police transmits the vehicle conditions to a traffic management system in a wireless mode to save illegal evidences, so that the illegal parking conditions can be conveniently rechecked when the follow-up illegal parking conditions exist.
For the related art, the inventor considers that when a traffic police shoots a vehicle by using a law enforcement recorder, if the environment is blocked by a wireless network, the acquired information cannot be transmitted in real time, and the information can only be stored in a temporary storage space, but the capacity of the temporary storage space is limited, and the situation that the environment image cannot be acquired after the temporary storage space is fully loaded possibly exists, so that evidence is lost, and the improvement is still available.
Disclosure of Invention
In order to reduce the occurrence of situations that the temporary storage space is fully loaded and the environment image cannot be acquired, the application provides a city vehicle monitoring method and system based on a law enforcement recorder.
In a first aspect, the present application provides a method for monitoring an urban vehicle based on a law enforcement recorder, which adopts the following technical scheme:
a city vehicle monitoring method based on law enforcement recorder includes:
acquiring a wireless transmission state;
acquiring the space occupancy rate of the temporary storage space when the wireless transmission state is consistent with a preset blocking state;
judging whether the space occupancy rate is larger than a preset reference occupancy rate or not;
if the space occupancy rate is not greater than the reference occupancy rate, maintaining the original state;
if the space occupancy rate is larger than the reference occupancy rate, obtaining an illegal vehicle image in the temporary storage space;
feature recognition is carried out on the illegal vehicle images so as to determine the front images of the vehicles with license plates and the corresponding license plates of the vehicles;
judging whether at least two front images of the vehicle exist under the same license plate number of the vehicle;
if at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the original state is maintained;
if at least two front images of the vehicle exist under the same license plate number of the vehicle, feature recognition is carried out in the front images of the vehicle to determine the type of the illegal vehicle, and front shooting points are determined according to the type of the illegal vehicle and the front images of the vehicle;
determining an optimal shooting point corresponding to the type of the illegal vehicle according to a preset point matching relation;
and determining a shooting deviation value according to the front shooting point and the optimal shooting point, determining the shooting deviation value with the smallest value according to a preset ordering rule, and deleting the front images of the vehicles except for the front images of the vehicles corresponding to the shooting deviation value under the same license plate number of the vehicles.
Through adopting above-mentioned technical scheme, when law enforcement record appearance is in unable wireless transmission and the great condition of temporary space occupation rate, can acquire and analyze the vehicle front image that acquires on same vehicle to confirm to preserve the image that can embody the vehicle condition of violating regulations the most, and delete other images, with the space occupation rate that reduces temporary space, thereby reduce the temporary space and fully load and can't take place to the condition of environment image.
Optionally, the step of determining the photographing deviation value according to the front photographing point and the optimal photographing point includes:
calculating according to the front shooting point and the optimal shooting point to determine the point spacing distance;
determining a standard front straight line and a standard detection point corresponding to the optimal shooting point according to a preset standard matching relation;
establishing a front shooting straight line according to the standard detection point and the front shooting point, and determining a deviation angle according to the front shooting straight line and the standard front straight line;
and calculating according to the point separation distance and the deviation angle to determine a shooting deviation value.
By adopting the technical scheme, a more accurate shooting deviation value can be determined according to the angle deviation and the distance deviation between the front shooting point and the optimal shooting point.
Optionally, after deleting the front image of the vehicle, the urban vehicle monitoring method based on the law enforcement recorder further comprises the following steps:
acquiring a vehicle side image corresponding to a vehicle front image;
counting according to all the side images of the vehicle to determine the number of the side images;
determining a correlation degree value according to the side images of the vehicle, and calculating according to the number of the side images and preset carry-over parameters to determine the carry-over number;
sorting the association degree values from big to small according to the sorting rule to determine side image sorting;
and determining the left-over number of vehicle side images in front according to the side image sequence, and deleting the rest vehicle side images.
By adopting the technical scheme, the images on the side face of the vehicle can be further deleted, so that the space occupancy rate of the temporary storage space is further released.
Optionally, the step of acquiring the vehicle side image includes:
acquiring a front image address of a left-behind front image of the vehicle;
extending to two sides of the address at the front image address to determine adjacent illegal vehicle images, and defining the adjacent illegal vehicle images as adjacent detection images;
determining a side image set corresponding to the type of the illegal vehicle according to a preset image matching relationship;
performing matching analysis on adjacent detection images in the side image set and all the images to determine a similarity ratio;
judging whether the similar ratio is larger than a preset similar ratio or not;
if the similar ratio is larger than the similar ratio, determining the adjacent detection image again according to the adjacent detection image until the similar ratio is not larger than the similar ratio;
if the similarity ratio is not greater than the like ratio, all the determined adjacent detection images are defined as vehicle side images.
By adopting the technical scheme, the side images acquired by the same vehicle are determined, so that the images needing to be left over and deleted can be analyzed later.
Optionally, the step of determining the association degree value according to the vehicle side image includes:
determining a side shooting point according to the type of the illegal vehicle and the side image of the vehicle;
determining shooting visual field areas corresponding to the side shooting points and the image definition according to a preset visual field matching relation;
determining a visual field overlapping area according to overlapping parts of the shooting visual field areas of the rest side shooting points and the single side shooting points, and calculating according to the visual field overlapping area and the corresponding shooting visual field areas of the rest side shooting points to determine an overlapping duty ratio;
calculating according to all overlapping duty ratios to determine the inclusion degree of the visual field;
and calculating according to the visual field inclusion degree and the image definition degree to determine the association degree value.
By adopting the technical scheme, a more accurate association degree value can be determined according to the visual field condition of each position.
Optionally, the method further includes a step of determining a legacy parameter, the step including:
determining a correlation degree value with the largest numerical value according to the ordering rule, and defining the correlation degree value as an upper limit correlation value;
determining a reference parameter corresponding to the upper limit association value according to a preset parameter matching relation;
calculating according to the reference parameters and the number of the side images to determine the reference number, determining the association degree value corresponding to the sorting in all the association degree values according to the reference number, and defining the association degree value as a boundary association value;
performing difference calculation according to the upper limit association value and the boundary association value to determine a deviation association value;
and determining a correction parameter corresponding to the deviation association value according to a preset correction matching relation, and calculating according to the correction parameter and the reference parameter to determine the legacy parameter.
By adopting the technical scheme, more accurate legacy parameters can be determined for use.
Optionally, after the determining of the legacy parameters, the urban vehicle monitoring method based on the law enforcement recorder further comprises:
determining side demand parameters corresponding to the number of the side images according to a preset number matching relation;
judging whether the determined legacy parameters are larger than the side demand parameters;
if the determined legacy parameters are greater than the side demand parameters, determining the legacy quantity according to the determined legacy parameters;
and if the determined legacy parameters are not greater than the side demand parameters, updating the side demand parameters to new legacy parameters.
By adopting the technical scheme, the quantity of the side images can be ensured to meet the minimum requirement, so that the situation that the vehicle violation condition cannot be presented due to the insufficient side images is reduced.
In a second aspect, the present application provides a city vehicle monitoring system based on a law enforcement recorder, which adopts the following technical scheme:
a law enforcement recorder-based city vehicle monitoring system comprising:
the acquisition module is used for acquiring the wireless transmission state;
the processing module is connected with the acquisition module and the judging module and is used for storing and processing information;
the judging module is connected with the acquisition module and the processing module and is used for judging information;
the acquisition module acquires the space occupancy rate of the temporary storage space when the judgment module judges that the wireless transmission state is consistent with the preset blocking state;
the judging module judges whether the space occupancy is larger than a preset reference occupancy;
if the judging module judges that the space occupancy rate is not greater than the reference occupancy rate, the original state is maintained;
if the judging module judges that the space occupancy rate is larger than the reference occupancy rate, the processing module outputs a capacity early warning signal and enables the acquiring module to acquire an illegal vehicle image in the temporary storage space;
the processing module performs feature recognition in the illegal vehicle image to determine a vehicle front image with a license plate number and a corresponding vehicle license plate number;
the judging module judges whether at least two front images of the vehicle exist under the same license plate number of the vehicle;
if the judging module judges that at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the original state is maintained;
if the judging module judges that at least two front images of the vehicle exist under the same license plate number of the vehicle, the processing module performs feature recognition in the front images of the vehicle to determine the type of the illegal vehicle, and determines a front shooting point according to the type of the illegal vehicle and the front images of the vehicle;
the processing module determines an optimal shooting point corresponding to the type of the illegal vehicle according to the preset point matching relation;
the processing module determines shooting deviation values according to the front shooting points and the optimal shooting points, determines the shooting deviation value with the smallest numerical value according to a preset ordering rule, and deletes the front images of the vehicles except the front images of the vehicles corresponding to the shooting deviation values under the same license plate number of the vehicles.
Through adopting above-mentioned technical scheme, when judging that law enforcement record appearance is in unable wireless transmission and the great condition of temporary storage space occupation rate, processing module can acquire and analyze the vehicle front image that obtains by the acquisition module on same vehicle to confirm that can embody the image of vehicle violation condition to save, and delete other images, in order to reduce the space occupation rate of temporary storage space, thereby reduce the temporary storage space and fully load and can't carry out the condition emergence of acquireing to the environment image.
In summary, the present application includes at least one of the following beneficial technical effects:
when wireless transmission fails and the space occupancy rate of the temporary storage space is large, repeated pictures can be automatically deleted to release part of the storage space, so that the situation that the temporary storage space is fully loaded and the environment image cannot be acquired is reduced;
the images which need to be left and deleted can be determined according to the conditions of the images on the side surfaces of the vehicle, so that the images can record the illegal conditions of the vehicle and simultaneously release temporary storage space;
the appropriate number of left-behind side images may be determined based on the vehicle condition to facilitate recording of the vehicle violation.
Drawings
Fig. 1 is a flow chart of a method of urban vehicle monitoring based on law enforcement recorders.
Fig. 2 is a flowchart of a shooting deviation value determination method.
Fig. 3 is a flowchart of a vehicle side image deletion processing method.
Fig. 4 is a flowchart of a vehicle side image definition method.
Fig. 5 is a flowchart of a correlation degree value determination method.
Fig. 6 is a flowchart of a legacy parameter determining method.
Fig. 7 is a flowchart of a legacy parameter updating method.
Fig. 8 is a block flow diagram of a law enforcement recorder-based city vehicle monitoring method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to fig. 1 to 8 and the embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The embodiment of the application discloses urban vehicle monitoring method based on law enforcement record appearance, in the use of law enforcement record appearance, when equipment is in the environment that can't wireless transmission, in case the space occupation rate of temporary storage space is great, can delete a plurality of repeated photos on same vehicle to release temporary storage space, thereby reduce the condition emergence that temporary storage space is fully loaded and can't acquire environmental image.
Referring to fig. 1, the method flow of the urban vehicle monitoring method based on the law enforcement recorder comprises the following steps:
step S100: and acquiring a wireless transmission state.
The wireless transmission state is a state that whether the law enforcement recorder can perform wireless transmission or not, including a state that the wireless transmission can be performed and a state that the wireless transmission cannot be performed, and the signal of the law enforcement recorder can be monitored to obtain the signal.
Step S101: and acquiring the space occupancy rate of the temporary storage space when the wireless transmission state is consistent with the preset blocking state.
The blocking state is a state which is set by staff and can not be transmitted wirelessly, and when the wireless transmission state is consistent with the blocking state, the image shot by the law enforcement recorder can not be directly uploaded to the processing platform, and the image is required to be stored in the temporary storage space; the space occupancy rate is a proportion value of the image in the temporary storage space to the whole space.
Step S102: and judging whether the space occupancy rate is larger than a preset reference occupancy rate.
The reference occupancy rate is the minimum space occupancy rate when the space occupancy rate of the temporary storage space is higher, which is set by a worker, and the purpose of judgment is to know whether the current temporary storage space can better store the shot image.
Step S1021: if the space occupancy rate is not greater than the reference occupancy rate, the original state is maintained.
When the space occupancy rate is not greater than the reference occupancy rate, the temporary storage space can be used for better storing the shot image, and no additional operation is needed at the moment.
Step S1022: and if the space occupancy rate is larger than the reference occupancy rate, acquiring an illegal vehicle image in the temporary storage space.
When the space occupancy rate is larger than the reference occupancy rate, the space of the temporary storage space is relatively tense, and in order to facilitate the storage of the images shot subsequently, the images in the temporary storage space need to be processed; the offending vehicle image is an image that already exists in the temporary storage space.
Step S103: feature recognition is performed on the images of the offending vehicles to determine the front images of the vehicles with license plates and the corresponding license plates of the vehicles.
The front image of the vehicle is an illegal vehicle image with license plates, the license plates can be identified by carrying out feature recognition on the license plates, and the license plates of the vehicle are the license plates in the determined front image of the vehicle.
Step S104: and judging whether at least two front images of the vehicle exist under the same license plate number of the vehicle.
The purpose of the judgment is to know whether there is a duplicate vehicle front image for the same vehicle to judge whether the duplicate image needs to be processed.
Step S1041: if at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the original state is maintained.
When at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the vehicle is indicated to have only one front image of the vehicle, and the images cannot be processed at the moment, so that additional operation is not needed.
Step S1042: if at least two front images of the vehicle exist under the same license plate number of the vehicle, feature recognition is carried out in the front images of the vehicle to determine the type of the illegal vehicle, and the front shooting points are determined according to the type of the illegal vehicle and the front images of the vehicle.
When at least two front images of the vehicle exist under the same license plate number of the vehicle, the situation that the front images of the same vehicle are repeated is indicated, and the repeated images are required to be processed at the moment; the type of the illegal vehicle is the type of the vehicle recorded in the front image of the vehicle, such as Audi A6 and the like, and can be determined by training a vehicle identification database in advance, importing the image and performing feature matching; the front shooting point is a shooting position corresponding to shooting of the illegal vehicle model so as to obtain a front image of the vehicle, and can be obtained through vehicle shooting simulation.
Step S105: and determining the optimal shooting point corresponding to the type of the illegal vehicle according to the preset point matching relation.
The optimal shooting points are shooting points when the situation of the vehicle with the illegal vehicle model can be effectively shot and recorded, and because the vehicle specifications of different illegal vehicle models are different, the corresponding optimal shooting points are different, and the point matching relationship between the two is determined by a plurality of experiments in advance by staff.
Step S106: and determining a shooting deviation value according to the front shooting point and the optimal shooting point, determining the shooting deviation value with the smallest value according to a preset ordering rule, and deleting the front images of the vehicles except for the front images of the vehicles corresponding to the shooting deviation value under the same license plate number of the vehicles.
The specific determination method is described below, and details are not repeated here; the sorting rule is a method which is set by staff and can sort the values, such as an bubbling method, the smallest shooting deviation value can be determined through the sorting rule, so that the front images of the vehicles shot by the front shooting point closest to the optimal shooting point can be determined, the front images of the rest vehicles except the front images of the vehicles can be deleted, the left front images of the vehicles can be effectively recorded under the condition of vehicle violation, and meanwhile, the space occupancy rate of the storage space can be effectively released, so that the storage space can be used for storing new vehicle images under the condition of violation.
Referring to fig. 2, the step of determining a photographing deviation value according to a front photographing point and an optimal photographing point includes:
step S200: and calculating according to the front shooting point and the optimal shooting point to determine the point spacing distance.
The point separation distance is a linear distance value between the front shooting point and the optimal shooting point.
Step S201: and determining a standard front straight line and a standard detection point corresponding to the optimal shooting point according to a preset standard matching relation.
The standard front straight line is a straight line which penetrates through the optimal shooting point and is perpendicular to the center point of the front of the vehicle, namely, an observation straight line which can be used for observing the front of the vehicle well in front of the vehicle, different optimal shooting points lead to different standard front straight lines, and the standard detection point is an intersection point of the standard front straight line and the front of the vehicle, and the standard matching relation among the standard front straight line, the standard front straight line and the vehicle is determined by a staff in advance according to multiple tests.
Step S202: and establishing a front shooting straight line according to the standard detection point and the front shooting point, and determining a deviation angle according to the front shooting straight line and the standard front straight line.
The front shooting straight line is a straight line which passes through the standard detection point and the front shooting point at the same time, and the deviation angle is an angle which is an acute angle formed by the front shooting straight line and the standard front straight line.
Step S203: and calculating according to the point separation distance and the deviation angle to determine a shooting deviation value.
The calculation formula is ∃ =alpha L+beta theta, wherein ∃ is a shooting deviation value, L is a point location distance, theta is a deviation angle, alpha is a calculation coefficient of the point location distance, beta is a calculation coefficient of the deviation angle, and specific numerical values of alpha and beta are set by staff according to actual conditions.
Referring to fig. 3, after deleting the front image of the vehicle, the urban vehicle monitoring method based on the law enforcement recorder further includes:
step S300: and acquiring a vehicle side image corresponding to the vehicle front image.
The vehicle side image is an image that is not a front image of the vehicle, which is obtained by photographing the vehicle corresponding to the front image of the vehicle, and a specific determination method is described below.
Step S301: counting is performed based on all of the vehicle side images to determine the number of side images.
The number of side images is the number of vehicle side images acquired by a single vehicle.
Step S302: and determining a correlation degree value according to the side images of the vehicle, and calculating according to the number of the side images and preset carry-over parameters to determine the carry-over number.
The association degree value is the association degree between each vehicle side image and the rest of vehicle side images, the greater the value is, the greater the inclusion of the vehicle side image on the rest of vehicle side images can be obtained through calculation through overlapping areas among the images, and a specific determination method is described below, and is not described in detail herein; the carry-over parameter is a coefficient ratio that needs to carry over the vehicle side image, the carry-over quantity is the quantity of the vehicle side image that needs to be carried over, for example, the quantity of the side image is 5, the carry-over parameter is 60%, the carry-over quantity is 3, and when the calculated carry-over parameter is not an integer, the calculated carry-over parameter can be obtained by rounding and calculating the last digit of the decimal point.
Step S303: and sorting the association degree values from large to small according to the sorting rule to determine the side image sorting.
The side image ranking is numerical ranking obtained by ranking the association degree values obtained by all the side images of the vehicle from big to small.
Step S304: and determining the left-over number of vehicle side images in front according to the side image sequence, and deleting the rest vehicle side images.
The vehicle side images with the front side images in sequence contain more characteristics of the rest images, namely the images can effectively record the vehicle violation conditions, the images are left behind at the moment, the vehicle side images with the back side images are deleted, so that temporary storage space can be released while the vehicle violation evidence is reserved.
Referring to fig. 4, the vehicle side image acquisition step includes:
step S400: a front image address of a left-behind front image of the vehicle is acquired.
The front image address is a storage address when the front image of the vehicle in the temporary storage space is stored.
Step S401: extending to both sides of the address at the front image address to determine adjacent offending vehicle images, and defining the adjacent offending vehicle images as adjacent detection images.
The images acquired for the violation of the same vehicle are typically at adjacent addresses, where adjacent detected images are defined for subsequent analysis of the images.
Step S402: and determining a side image set corresponding to the type of the illegal vehicle according to the preset image matching relation.
The side image set is only required to be an image which can be shot when the image acquisition is carried out on the vehicle with the illegal vehicle model, and the image matching relation between the side image set and the image matching relation is recorded and stored in advance by a staff.
Step S403: and carrying out matching analysis on adjacent detection images and all images in the side image set to determine the similarity ratio.
The similarity ratio is the similarity ratio of the adjacent detected image to any image in the set of side images.
Step S404: judging whether the similarity ratio is larger than a preset similarity ratio.
The like ratio is the minimum similarity ratio of images which are set by staff and can be shot by the same vehicle model, and the purpose of judgment is to know whether the currently determined adjacent detection image is a vehicle side image of the vehicle corresponding to the illegal vehicle model.
Step S4041: if the similar ratio is larger than the similar ratio, determining the adjacent detection images again according to the adjacent detection images until the similar ratio is not larger than the similar ratio.
And when the similar ratio is larger than the similar ratio, the adjacent detection image is a determined vehicle side image of the vehicle, and the adjacent detection image is updated according to the adjacent detection image until no condition that the adjacent ratio is larger than the similar ratio exists, namely, until the condition that the adjacent detection image is determined to be the other vehicle.
Step S4042: if the similarity ratio is not greater than the like ratio, all the determined adjacent detection images are defined as vehicle side images.
When the situation that the similar ratio is larger than the similar ratio does not exist, the fact that the two vehicles are not of the same model is indicated, namely, the critical positions of the images of the two illegal vehicles are reached at the moment, and the currently determined adjacent detection images are determined to be the side images of the vehicles.
Referring to fig. 5, the step of determining the association degree value from the vehicle side image includes:
step S500: and determining the side shooting point according to the type of the illegal vehicle and the side image of the vehicle.
The side shooting points are shooting points when the vehicle side images can be obtained by shooting the illegal vehicle models.
Step S501: and determining shooting visual field areas corresponding to the side shooting points and the image definition according to a preset visual field matching relation.
The shooting visual field area is the range area of the vehicle shot by the side shooting point, when the side shooting point is far away from the vehicle, the shooting visual field area is larger, the image definition degree is the definition degree of the image shot by the law enforcement recorder at the side shooting point aiming at details, and when the side shooting point is far away from the vehicle, the image definition degree is poorer.
Step S502: and determining a visual field overlapping area at the single side shooting point according to the overlapping part of the shooting visual field area and the shooting visual field areas of the rest side shooting points, and calculating according to the visual field overlapping area and the corresponding shooting visual field areas of the rest side shooting points to determine the overlapping duty ratio.
The overlapping area of the visual field is an overlapping portion of the shooting visual field areas determined by the two side shooting points, the overlapping ratio is a ratio of the overlapping area of the visual field to the shooting visual field areas obtained by the rest of the side shooting points, for example, the shooting visual field area of the current side shooting point is a front wheel to a rear wheel, the shooting visual field area of one rest of the side shooting points is a front wheel to the middle part of the automobile, and the overlapping ratio is 100%.
Step S503: a calculation is made to determine the extent of field inclusion based on all of the overlapping duty cycles.
The view inclusion degree is a numerical value reflecting the inclusion degree of the vehicle side image acquired by the current side shooting point on the other vehicle side images, and the view inclusion degree is acquired by calculating the average value after all overlapping duty ratios are added.
Step S504: and calculating according to the visual field inclusion degree and the image definition degree to determine the association degree value.
The calculation formula is delta=gammam+δn, wherein delta is a correlation degree value, M is a visual field containing degree, N is an image definition degree, gamma is a calculation coefficient of the visual field containing degree, delta is a calculation coefficient of the image definition degree, and gamma and delta are constant value coefficients set in advance by staff.
Referring to fig. 6, further comprising a determination step of legacy parameters, the step comprising:
step S600: and determining the association degree value with the largest numerical value according to the ordering rule, and defining the association degree value as an upper limit association value.
The upper bound association values are defined to facilitate differentiation between different association degree values for subsequent analysis.
Step S601: and determining a reference parameter corresponding to the upper limit association value according to a preset parameter matching relation.
The reference parameter is the minimum legacy parameter required by the upper limit association value, for example, the upper limit association value is 100%, that is, one image contains all other images, at this time, the reference parameter can be lower, and the parameter matching relationship between the two is determined in advance by staff.
Step S602: and calculating according to the reference parameters and the number of the side images to determine the reference number, determining the association degree value corresponding to the ranking in all the association degree values according to the reference number, and defining the association degree value as a boundary association value.
The reference number is the minimum number of the required side images of the vehicle, and is determined by multiplying the number of the side images by the reference parameter; the boundary association value is the association degree value of the values corresponding to the first reference number in the ranking of all the association degree values, for example, the reference number is 2.
Step S603: and carrying out difference calculation according to the upper limit association value and the boundary association value to determine a deviation association value.
The deviation association value is the difference value of association degree values corresponding to the side images of the vehicle determined under the reference quantity, and the boundary association value is subtracted from the upper limit association value to determine.
Step S604: and determining a correction parameter corresponding to the deviation association value according to a preset correction matching relation, and calculating according to the correction parameter and the reference parameter to determine the legacy parameter.
The correction parameters are coefficients for correcting the legacy parameters, the deviation degrees of the vehicle side images determined under the reference quantity are different according to different deviation association values, when the deviation association values are larger, the details of the vehicle illegal positions are described to be more, at the moment, some vehicle detail images are required to be left, the correction parameters are larger, and the correction parameters plus the reference parameters can be used for determining more proper legacy parameters.
Referring to fig. 7, after the legacy parameters are determined, the law enforcement recorder-based urban vehicle monitoring method further comprises:
step S700: and determining the side demand parameters corresponding to the number of the side images according to the preset number matching relation.
The side demand parameter is the minimum carry-over parameter required under the number of side images, and the number matching relation between the side demand parameter and the minimum carry-over parameter is determined in advance by staff.
Step S701: it is determined whether the determined legacy parameter is greater than the side demand parameter.
The purpose of the determination is to know whether the currently determined legacy parameters meet the requirements.
Step S7011: and if the determined legacy parameter is greater than the side demand parameter, determining the legacy quantity according to the determined legacy parameter.
When the determined legacy parameters are larger than the side demand parameters, the currently determined legacy parameters are indicated to meet the requirements, and the determination of the legacy quantity is performed normally.
Step S7012: and if the determined legacy parameters are not greater than the side demand parameters, updating the side demand parameters to new legacy parameters.
When the determined legacy parameters are not greater than the side demand parameters, it is indicated that the vehicle side images meeting the quantity demand cannot be obtained through the legacy parameters, and the legacy parameters are updated by using the side demand parameters so that the legacy vehicle side images meet the demand.
Referring to fig. 8, based on the same inventive concept, an embodiment of the present invention provides a city vehicle monitoring system based on a law enforcement recorder, comprising:
the acquisition module is used for acquiring the wireless transmission state;
the processing module is connected with the acquisition module and the judging module and is used for storing and processing information;
the judging module is connected with the acquisition module and the processing module and is used for judging information;
the acquisition module acquires the space occupancy rate of the temporary storage space when the judgment module judges that the wireless transmission state is consistent with the preset blocking state;
the judging module judges whether the space occupancy is larger than a preset reference occupancy;
if the judging module judges that the space occupancy rate is not greater than the reference occupancy rate, the original state is maintained;
if the judging module judges that the space occupancy rate is larger than the reference occupancy rate, the processing module outputs a capacity early warning signal and enables the acquiring module to acquire an illegal vehicle image in the temporary storage space;
the processing module performs feature recognition in the illegal vehicle image to determine a vehicle front image with a license plate number and a corresponding vehicle license plate number;
the judging module judges whether at least two front images of the vehicle exist under the same license plate number of the vehicle;
if the judging module judges that at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the original state is maintained;
if the judging module judges that at least two front images of the vehicle exist under the same license plate number of the vehicle, the processing module performs feature recognition in the front images of the vehicle to determine the type of the illegal vehicle, and determines a front shooting point according to the type of the illegal vehicle and the front images of the vehicle;
the processing module determines an optimal shooting point corresponding to the type of the illegal vehicle according to the preset point matching relation;
the processing module determines shooting deviation values according to the front shooting points and the optimal shooting points, determines the shooting deviation value with the smallest numerical value according to a preset ordering rule, and deletes the front images of the vehicles except the front images of the vehicles corresponding to the shooting deviation values under the same license plate number of the vehicles;
the shooting deviation value determining module is used for determining more accurate shooting deviation values;
the side deleting control module is used for controlling the deleting of partial images of the side images of the vehicle;
the side image analysis module is used for analyzing the acquired images to determine more accurate side images of the vehicle;
the association degree value determining module is used for determining a more accurate association degree value for use;
the system comprises a legacy parameter determining module, a legacy parameter determining module and a control module, wherein the legacy parameter determining module is used for determining more proper legacy parameters for use;
and the legacy parameter updating module is used for analyzing and judging the legacy parameters so as to update the legacy parameters when the legacy parameters do not meet the requirements.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
Claims (7)
1. A method for monitoring urban vehicles based on law enforcement recorders, comprising:
acquiring a wireless transmission state;
acquiring the space occupancy rate of the temporary storage space when the wireless transmission state is consistent with a preset blocking state;
judging whether the space occupancy rate is larger than a preset reference occupancy rate or not;
if the space occupancy rate is not greater than the reference occupancy rate, maintaining the original state;
if the space occupancy rate is larger than the reference occupancy rate, obtaining an illegal vehicle image in the temporary storage space;
feature recognition is carried out on the illegal vehicle images so as to determine the front images of the vehicles with license plates and the corresponding license plates of the vehicles;
judging whether at least two front images of the vehicle exist under the same license plate number of the vehicle;
if at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the original state is maintained;
if at least two front images of the vehicle exist under the same license plate number of the vehicle, feature recognition is carried out in the front images of the vehicle to determine the type of the illegal vehicle, and front shooting points are determined according to the type of the illegal vehicle and the front images of the vehicle;
determining an optimal shooting point corresponding to the type of the illegal vehicle according to a preset point matching relation;
determining a shooting deviation value according to the front shooting point and the optimal shooting point, determining the shooting deviation value with the smallest numerical value according to a preset ordering rule, and deleting the front images of the vehicles except for the front images of the vehicles corresponding to the shooting deviation value under the same license plate number of the vehicles;
the step of determining the photographing deviation value according to the front photographing point and the optimal photographing point includes:
calculating according to the front shooting point and the optimal shooting point to determine the point spacing distance;
determining a standard front straight line and a standard detection point corresponding to the optimal shooting point according to a preset standard matching relation;
establishing a front shooting straight line according to the standard detection point and the front shooting point, and determining a deviation angle according to the front shooting straight line and the standard front straight line;
and calculating according to the point separation distance and the deviation angle to determine a shooting deviation value.
2. The law enforcement recorder-based urban vehicle monitoring method of claim 1, wherein after the vehicle front image is deleted, the law enforcement recorder-based urban vehicle monitoring method further comprises:
acquiring a vehicle side image corresponding to a vehicle front image;
counting according to all the side images of the vehicle to determine the number of the side images;
determining a correlation degree value according to the side images of the vehicle, and calculating according to the number of the side images and preset carry-over parameters to determine the carry-over number;
sorting the association degree values from big to small according to the sorting rule to determine side image sorting;
and determining the left-over number of vehicle side images in front according to the side image sequence, and deleting the rest vehicle side images.
3. The law enforcement recorder-based city vehicle monitoring method of claim 2, wherein the vehicle side image acquiring step comprises:
acquiring a front image address of a left-behind front image of the vehicle;
extending to two sides of the address at the front image address to determine adjacent illegal vehicle images, and defining the adjacent illegal vehicle images as adjacent detection images;
determining a side image set corresponding to the type of the illegal vehicle according to a preset image matching relationship;
performing matching analysis on adjacent detection images in the side image set and all the images to determine a similarity ratio;
judging whether the similar ratio is larger than a preset similar ratio or not;
if the similar ratio is larger than the similar ratio, determining the adjacent detection image again according to the adjacent detection image until the similar ratio is not larger than the similar ratio;
if the similarity ratio is not greater than the like ratio, all the determined adjacent detection images are defined as vehicle side images.
4. The law enforcement recorder-based city vehicle monitoring method of claim 2 wherein the step of determining the association level value from the vehicle side images comprises:
determining a side shooting point according to the type of the illegal vehicle and the side image of the vehicle;
determining shooting visual field areas corresponding to the side shooting points and the image definition according to a preset visual field matching relation;
determining a visual field overlapping area according to overlapping parts of the shooting visual field areas of the rest side shooting points and the single side shooting points, and calculating according to the visual field overlapping area and the corresponding shooting visual field areas of the rest side shooting points to determine an overlapping duty ratio;
calculating according to all overlapping duty ratios to determine the inclusion degree of the visual field;
and calculating according to the visual field inclusion degree and the image definition degree to determine the association degree value.
5. The law enforcement recorder-based city vehicle monitoring method of claim 2, further comprising the step of determining legacy parameters, the step comprising:
determining a correlation degree value with the largest numerical value according to the ordering rule, and defining the correlation degree value as an upper limit correlation value;
determining a reference parameter corresponding to the upper limit association value according to a preset parameter matching relation;
calculating according to the reference parameters and the number of the side images to determine the reference number, determining the association degree value corresponding to the sorting in all the association degree values according to the reference number, and defining the association degree value as a boundary association value;
performing difference calculation according to the upper limit association value and the boundary association value to determine a deviation association value;
and determining a correction parameter corresponding to the deviation association value according to a preset correction matching relation, and calculating according to the correction parameter and the reference parameter to determine the legacy parameter.
6. The law enforcement recorder-based city vehicle monitoring method of claim 5, wherein after the legacy parameter is determined, the law enforcement recorder-based city vehicle monitoring method further comprises:
determining side demand parameters corresponding to the number of the side images according to a preset number matching relation;
judging whether the determined legacy parameters are larger than the side demand parameters;
if the determined legacy parameters are greater than the side demand parameters, determining the legacy quantity according to the determined legacy parameters;
and if the determined legacy parameters are not greater than the side demand parameters, updating the side demand parameters to new legacy parameters.
7. Urban vehicle monitoring system based on law enforcement recorder, characterized by comprising:
the acquisition module is used for acquiring the wireless transmission state;
the processing module is connected with the acquisition module and the judging module and is used for storing and processing information;
the judging module is connected with the acquisition module and the processing module and is used for judging information;
the acquisition module acquires the space occupancy rate of the temporary storage space when the judgment module judges that the wireless transmission state is consistent with the preset blocking state;
the judging module judges whether the space occupancy is larger than a preset reference occupancy;
if the judging module judges that the space occupancy rate is not greater than the reference occupancy rate, the original state is maintained;
if the judging module judges that the space occupancy rate is larger than the reference occupancy rate, the processing module outputs a capacity early warning signal and enables the acquiring module to acquire an illegal vehicle image in the temporary storage space;
the processing module performs feature recognition in the illegal vehicle image to determine a vehicle front image with a license plate number and a corresponding vehicle license plate number;
the judging module judges whether at least two front images of the vehicle exist under the same license plate number of the vehicle;
if the judging module judges that at least two front images of the vehicle do not exist under the same license plate number of the vehicle, the original state is maintained;
if the judging module judges that at least two front images of the vehicle exist under the same license plate number of the vehicle, the processing module performs feature recognition in the front images of the vehicle to determine the type of the illegal vehicle, and determines a front shooting point according to the type of the illegal vehicle and the front images of the vehicle;
the processing module determines an optimal shooting point corresponding to the type of the illegal vehicle according to the preset point matching relation;
the processing module determines shooting deviation values according to the front shooting points and the optimal shooting points, determines the shooting deviation value with the smallest numerical value according to a preset ordering rule, and deletes the front images of the vehicles except the front images of the vehicles corresponding to the shooting deviation values under the same license plate number of the vehicles;
the step of determining the shooting deviation value by the processing module according to the front shooting point and the optimal shooting point comprises the following steps:
the processing module calculates according to the front shooting point and the optimal shooting point to determine the point spacing distance;
the processing module determines a standard front straight line and a standard detection point corresponding to the optimal shooting point according to a preset standard matching relation;
the processing module establishes a front shooting straight line according to the standard detection point and the front shooting point, and determines a deviation angle according to the front shooting straight line and the standard front straight line;
the processing module calculates according to the point separation distance and the deviation angle to determine a shooting deviation value.
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Denomination of invention: A City Vehicle Monitoring Method and System Based on Law Enforcement Recorder Granted publication date: 20231226 Pledgee: Bank of Beijing Co.,Ltd. Jinan Branch Pledgor: CHINA ZE ELECTRONIC CO.,LTD. Registration number: Y2024370000029 |