CN112529955A - Road normalization and speed recovery method and device for expressway - Google Patents
Road normalization and speed recovery method and device for expressway Download PDFInfo
- Publication number
- CN112529955A CN112529955A CN202011422658.3A CN202011422658A CN112529955A CN 112529955 A CN112529955 A CN 112529955A CN 202011422658 A CN202011422658 A CN 202011422658A CN 112529955 A CN112529955 A CN 112529955A
- Authority
- CN
- China
- Prior art keywords
- road
- speed
- data
- coordinate system
- normalization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000011084 recovery Methods 0.000 title claims abstract description 30
- 238000010606 normalization Methods 0.000 title claims abstract description 28
- 238000009825 accumulation Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000009434 installation Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 8
- 230000000877 morphologic effect Effects 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 9
- 230000006870 function Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000002592 echocardiography Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000003628 erosive effect Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/589—Velocity or trajectory determination systems; Sense-of-movement determination systems measuring the velocity vector
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/203—Drawing of straight lines or curves
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Mathematics (AREA)
- Mathematical Physics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Operations Research (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a method and a device for road normalization and speed recovery of an expressway, wherein the method comprises the following steps: accumulating the radar data, wherein the accumulated data is the data in the time period from t1 to t2
S (x, y) represents a velocity accumulation value at a coordinate (x, y) position in a coordinate system; performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road; performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y); performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x); and recovering the speed of the moving target according to the fitted curve y ═ f (x). The radar and road arbitrary point included angle is calculated through learning of the road trend, and then basis is provided for more accurate speed measurement.
Description
Technical Field
The invention belongs to the technical field of radar speed measurement, and particularly relates to a method and a device for road normalization and speed recovery of an expressway.
Background
Speed measuring radar is the main means for measuring the speed of moving targets on roads at present. But due to radar principle limitations it measures the radial velocity of the target and the radar, not the actual velocity. The reason for this is that besides the lateral movement of some targets, more of the targets come from the road itself and are not completely perpendicular to the radar device, so that the speed measurement is not accurate enough.
Disclosure of Invention
In order to solve the problem that the speed measurement of a moving target on a road by a speed measuring radar is not accurate enough at present, the embodiment of the application provides a road normalization and speed recovery method and a device for an expressway.
In a first aspect, an embodiment of the present application provides a method for road normalization and speed recovery on an expressway, including:
data accumulation is performed on the radar data, at t1-t2The accumulated data in the time period is
S (x, y) represents a speed accumulated value on a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at the time t, S (t) comprises speed and position information of a moving target, and road full-coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is a radar installation position;
performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road;
performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y);
performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x);
and recovering the speed of the moving target according to the fitted curve y ═ f (x).
Wherein, the recovering the speed of the moving object according to the fitted curve y ═ f (x) includes:
calculating a tangent slope angle θ (y) at any point on the curve y ═ f (x);
when a radial velocity v of a moving object is detected within a y-point preset range, a true velocity of the moving object is vr ═ v/abs (cos (θ (y)).
Wherein the skeletonizing operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y) comprises:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
performing skeletonization operation in mathematical morphology to obtain Sg (x, y).
Wherein the parameter t1And t2Is a preset parameter.
The coordinate system is a Cartesian coordinate system or a polar coordinate system, and the y axis of the coordinate system is the radar detection direction.
In a second aspect, the present application provides a road normalization and speed recovery apparatus for an expressway, including:
a data accumulation unit for: data accumulation is performed on the radar data, at t1-t2The accumulated data in the time period is
S (x, y) represents a speed accumulated value on a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at the time t, S (t) comprises speed and position information of a moving target, and road full-coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is a radar installation position;
a binarization unit for: performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road;
a skeletonization unit to: performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y);
a fitting unit for: performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x);
a recovery unit to: and recovering the speed of the moving target according to the fitted curve y ═ f (x).
Wherein the recovery unit is configured to:
calculating a tangent slope angle θ (y) at any point on the curve y ═ f (x);
when a radial velocity v of a moving object is detected within a y-point preset range, a true velocity of the moving object is vr ═ v/abs (cos (θ (y)).
Wherein the skeletonization unit is configured to:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
performing skeletonization operation in mathematical morphology to obtain Sg (x, y).
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is used for implementing the steps of any one of the above methods when executed by a processor.
In a fourth aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the program.
The method and the device for road normalization and speed recovery of the expressway in the embodiment of the application have the following beneficial effects:
the method for road normalization and speed recovery of the highway comprises the following steps: data accumulation is performed on the radar data, at t1-t2The accumulated data in the time period is
S (x, y) represents the velocity accumulation value at the coordinate (x, y) position in the coordinate system, S (t) is the traffic condition at the time t,s (t) acquiring road full-coverage information through accumulation, wherein the speed and position information comprises a moving target; wherein, the origin (0, 0) of the coordinate system is a radar installation position; performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road; performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y); performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x); and recovering the speed of the moving target according to the fitted curve y ═ f (x).
The radar and road arbitrary point included angle is calculated through learning of the road trend, and then basis is provided for more accurate speed measurement.
Drawings
Fig. 1 is a schematic flow chart of a road normalization and speed recovery method for an expressway according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a relationship between coordinates and radar installation in the method for road normalization and speed recovery on an expressway according to the embodiment of the present application;
fig. 3 is a schematic diagram illustrating speed recovery in a road normalization and speed recovery method for an expressway according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a road normalization and speed recovery device for an expressway according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application is further described with reference to the following figures and examples.
In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the invention, which may be combined or substituted for various embodiments, and this application is therefore intended to cover all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be considered to include an embodiment that includes one or more of all other possible combinations of A, B, C, D, even though this embodiment may not be explicitly recited in text below.
The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Speed measuring radar is the main means for measuring the speed of moving targets on roads at present. But due to radar principle limitations it measures the radial velocity of the target and the radar, not the actual velocity. The reason for this is that in addition to the lateral movement of some objects, more of the road itself is not perfectly perpendicular to the radar installation. The invention relates to a road normalization and speed recovery method suitable for radar speed measurement, which can calculate an included angle between a radar and any point of a road through learning of the road trend, and further provides a basis for more accurate speed measurement.
The invention discloses a road normalization and speed recovery method suitable for a highway speed measuring radar, which can correct speed deviation caused by that the radar can only measure radial speed.
As shown in fig. 1 to 3, the method for road normalization and speed recovery for an expressway of the present application includes the steps of: s101, accumulating data of the radar data at t1-t2The accumulated data in the time period is
S (x, y) represents the velocity accumulation value at the coordinate (x, y) position in the coordinate system, S (t) is the traffic condition at the time t, S (x, y) ((x, y))t) speed and position information of the moving target are included, and road full-coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is a radar installation position; s103, performing binarization processing on the accumulated data S (x, y) by using a binarization method, and as a result, Sw (x, y) indicates that the location is a road when Sw (x, y) is 1 and indicates that the location is a non-road when Sw (x, y) is 0; s105, performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y); s107, performing curve fitting on Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x); and S109, recovering the speed of the moving target according to the fitted curve y ═ f (x). Each step is described below.
S101, accumulating data of the radar data at t1-t2The accumulated data in the time period is
S (x, y) represents a speed accumulated value on a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at the time t, S (t) comprises speed and position information of a moving target, and road full-coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is the radar installation position.
In this step, data echoes are accumulated, and data accumulation is performed on radar data at a specific point. Including but not limited to cartesian, polar, etc. The application is described by taking a cartesian coordinate system as an example, and the y-axis of the coordinate system is the radar detection direction. Fig. 2 shows the coordinate and radar installation relationship, the origin (0, 0) of the coordinate system is the radar installation position, and the radar antenna is oriented in the positive y-axis direction. For the selection of the parameters t1 and t2, the parameters t1 and t2 are preset parameters based on the time period required by the user. When the cumulative echo is observed to be constant in area in the plane S (x, y), it is taken as the standard.
S103, the accumulated data S (x, y) is binarized by a binarization method, and as a result, Sw (x, y) indicates that the location is a road when Sw (x, y) is 1 and indicates that the location is a non-road when Sw (x, y) is 0.
In this step, S (x, y) is binarized by using the tsu method, which is an algorithm for determining an image binary segmentation threshold, and is also called a maximum inter-class difference method (OTSU). The image is divided into a background part and a foreground part according to the gray characteristic of the image. Since the variance is a measure of the uniformity of the gray distribution, the larger the inter-class variance between the background and the foreground is, the larger the difference between the two parts constituting the image is, and the smaller the difference between the two parts is when part of the foreground is mistaken for the background or part of the background is mistaken for the foreground. Thus, a segmentation that maximizes the inter-class variance means that the probability of false positives is minimized.
S105, performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y).
Mathematical morphology (Mathematical morphology) is an image analysis subject based on lattice theory and topology, and is a basic theory of Mathematical morphology image processing. The basic operations include: erosion and expansion, opening and closing operation, skeleton extraction, limit erosion, hit-miss transformation, morphological gradient, Top-hat transformation, particle analysis, watershed transformation and the like.
In some embodiments, this step comprises: performing morphological closing operation on Sw (x, y) to remove the influence of noise; performing skeletonization operation in mathematical morphology to obtain Sg (x, y).
S107, curve fitting is performed on Sg (x, y) by the least square method, and a fitted curve y is obtained as f (x).
Least squares (also known as the least squares method) is a mathematical optimization technique. It finds the best functional match of the data by minimizing the sum of the squares of the errors. Unknown data can be easily obtained by the least square method, and the sum of squares of errors between these obtained data and actual data is minimized.
And S109, recovering the speed of the moving target according to the fitted curve y ═ f (x).
As shown in fig. 3, this step includes: calculating a tangent slope angle θ (y) at any point on the curve y ═ f (x); when the radial velocity v of the moving target is detected in the y-point preset range, the real velocity of the moving target is vr ═ v/abs (cos (θ (y)). the size of the preset range can be determined according to actual needs.
The invention discloses a road normalization method suitable for radar speed measurement, which can calculate the included angle between a radar and any point of a road through learning of the road trend so as to provide a basis for more accurate speed measurement, and obtain a more accurate speed value after the original speed is recovered.
In the method, the first step is to accumulate data echoes, and the step aims to acquire the position information of a road by accumulating the data echoes for a certain time; the second step is data binarization, which aims to extract the road information primarily in a plane coordinate; the third step is data skeletonization, which aims to abstract the binarized information to obtain the line description of the road, and simultaneously, the step can filter the influence of noise in the echo accumulation and binarization processes; the fourth step is function fitting, which aims to prevent distortion formed by noise in skeletonized data and make road description closer to the real situation; then, a tangent slope list is obtained, and the purpose is to obtain the relative included angle of any point of the road relative to the radar installation position (coordinate origin); and finally, calculating the real speed by utilizing the geometric relation between the radial speed of the radar and the real speed for speed recovery.
As shown in fig. 4, the apparatus for road normalization and speed recovery for an expressway of the present application includes: a data accumulation unit 201 for: data accumulation is performed on the radar data, at t1-t2The accumulated data in the time period is
S (x, y) represents a speed accumulated value on a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at the time t, S (t) comprises speed and position information of a moving target, and road full-coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is a radar installation position;
a binarization unit 202 for: performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road;
a skeletonization unit 203 for: performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y);
a fitting unit 204 for: performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x);
a recovery unit 205 for: and recovering the speed of the moving target according to the fitted curve y ═ f (x).
Wherein the recovery unit is configured to:
calculating a tangent slope angle θ (y) at any point on the curve y ═ f (x);
when a radial velocity v of a moving object is detected within a y-point preset range, a true velocity of the moving object is vr ═ v/abs (cos (θ (y)).
Wherein the skeletonization unit is configured to:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
performing skeletonization operation in mathematical morphology to obtain Sg (x, y).
In the present application, the embodiments of the apparatus for normalizing and recovering speed on a highway are substantially similar to the embodiments of the method for normalizing and recovering speed on a highway, and reference is made to the description of the embodiments of the method for normalizing and recovering speed on a highway.
It is clear to a person skilled in the art that the solution according to the embodiments of the invention can be implemented by means of software and/or hardware. The "unit" and "module" in this specification refer to software and/or hardware that can perform a specific function independently or in cooperation with other components, where the hardware may be, for example, an FPGA (Field-Programmable Gate Array), an IC (Integrated Circuit), or the like.
Each processing unit and/or module according to the embodiments of the present invention may be implemented by an analog circuit that implements the functions described in the embodiments of the present invention, or may be implemented by software that executes the functions described in the embodiments of the present invention.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for road normalization and speed recovery for a highway. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers, as shown in fig. 5. The computer device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The computer apparatus of the present application comprises a processor 401, a memory 402, an input device 403 and an output device 404. The processor 401, memory 402, input device 403, and output device 404 may be connected by a bus 405 or otherwise. The memory 402 has stored thereon a computer program which is executable on the processor 401, and the processor 401 when executing the program performs the road normalization and speed recovery method steps for a highway as described above.
The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the data processing computer apparatus, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick or other input device. The output devices 404 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. Display devices may include, but are not limited to, Liquid Crystal Displays (LCDs), Light Emitting Diode (LED) displays, plasma displays, and touch screens.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
All functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A road normalization and speed recovery method for an expressway is characterized by comprising the following steps:
data accumulation is performed on the radar data, at t1-t2The accumulated data in the time period is
S (x, y) represents the velocity accumulation value at the coordinate (x, y) position in the coordinate system, S (t) is the traffic condition at the time t, S (t)The method comprises the steps of obtaining road full coverage information through accumulation, wherein the speed and position information comprises a moving target; wherein, the origin (0, 0) of the coordinate system is a radar installation position;
performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road;
performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y);
performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x);
and recovering the speed of the moving target according to the fitted curve y ═ f (x).
2. The method for road normalization and speed recovery for an expressway according to claim 1, wherein the recovering the speed of the moving object according to the fitted curve y ═ f (x) comprises:
calculating a tangent slope angle θ (y) at any point on the curve y ═ f (x);
when a radial velocity v of a moving object is detected within a y-point preset range, a true velocity of the moving object is vr ═ v/abs (cos (θ (y)).
3. The method for road normalization and speed restoration for a highway according to claim 1 or 2, wherein the skeletonizing operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y) comprises:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
performing skeletonization operation in mathematical morphology to obtain Sg (x, y).
4. Method for road normalization and speed restoration for motorways according to claim 1 or 2, characterized in that the parameter t is1And t2Is a preset parameter.
5. The method for road normalization and speed recovery of an expressway according to claim 1 or 2, wherein the coordinate system is a cartesian coordinate system or a polar coordinate system, and the y-axis of the coordinate system is a radar detection direction.
6. A road normalization and speed recovery device for an expressway, comprising:
a data accumulation unit for: data accumulation is performed on the radar data, at t1-t2The accumulated data in the time period is
S (x, y) represents a speed accumulated value on a coordinate (x, y) position in a coordinate system, S (t) is a traffic condition at the time t, S (t) comprises speed and position information of a moving target, and road full-coverage information is obtained through accumulation; wherein, the origin (0, 0) of the coordinate system is a radar installation position;
a binarization unit for: performing binarization processing on the accumulated data S (x, y) by using a binarization method, wherein the result is Sw (x, y), when Sw (x, y) is 1, the position is indicated as a road, and when Sw (x, y) is 0, the position is indicated as a non-road;
a skeletonization unit to: performing skeletonization operation in mathematical morphology on the Sw (x, y) to obtain Sg (x, y);
a fitting unit for: performing curve fitting on the Sg (x, y) by using a least square method to obtain a fitted curve y ═ f (x);
a recovery unit to: and recovering the speed of the moving target according to the fitted curve y ═ f (x).
7. The road normalization and speed restoration device for the expressway according to claim 6, wherein the restoration unit is configured to:
calculating a tangent slope angle θ (y) at any point on the curve y ═ f (x);
when a radial velocity v of a moving object is detected within a y-point preset range, a true velocity of the moving object is vr ═ v/abs (cos (θ (y)).
8. The device for road normalization and speed restoration for an expressway according to claim 6 or 7, wherein the skeletonizing unit is configured to:
performing morphological closing operation on Sw (x, y) to remove the influence of noise;
performing skeletonization operation in mathematical morphology to obtain Sg (x, y).
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-5 are implemented when the program is executed by the processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011422658.3A CN112529955B (en) | 2020-12-08 | 2020-12-08 | Road normalization and speed recovery method and device for expressway |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011422658.3A CN112529955B (en) | 2020-12-08 | 2020-12-08 | Road normalization and speed recovery method and device for expressway |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112529955A true CN112529955A (en) | 2021-03-19 |
| CN112529955B CN112529955B (en) | 2023-09-29 |
Family
ID=74998255
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011422658.3A Active CN112529955B (en) | 2020-12-08 | 2020-12-08 | Road normalization and speed recovery method and device for expressway |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112529955B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001289944A (en) * | 2000-04-06 | 2001-10-19 | Nec Corp | Track tracking method and device therefor |
| CN101718870A (en) * | 2009-11-13 | 2010-06-02 | 西安电子科技大学 | High-speed weak target flight path detection method of image field |
| WO2012136494A1 (en) * | 2011-04-02 | 2012-10-11 | Valeo Schalter Und Sensoren Gmbh | Method for determining a correction value for the measurement of a target angle with a radar device, driver assistance system and motor vehicle |
| CN106054174A (en) * | 2015-04-06 | 2016-10-26 | 通用汽车环球科技运作有限责任公司 | Fusion method for cross traffic application using radars and camera |
| CN108909721A (en) * | 2018-04-28 | 2018-11-30 | 南通职业大学 | A kind of vehicle yaw angle calculation method based on millimetre-wave radar |
| CN108957475A (en) * | 2018-06-26 | 2018-12-07 | 东软集团股份有限公司 | A kind of Method for Road Boundary Detection and device |
| CN109002795A (en) * | 2018-07-13 | 2018-12-14 | 清华大学 | Method for detecting lane lines, device and electronic equipment |
| CN109102702A (en) * | 2018-08-24 | 2018-12-28 | 南京理工大学 | Vehicle speed measuring method based on video encoder server and Radar Signal Fusion |
| CN110210298A (en) * | 2019-04-25 | 2019-09-06 | 南开大学 | A kind of circuitous path information extraction and representation method based on the aerial visual field |
| FR3086066A1 (en) * | 2018-09-13 | 2020-03-20 | Idemia Identity & Security France | METHOD AND DEVICE FOR MONITORING THE OPERATION OF A ROAD RADAR. |
| US20200209402A1 (en) * | 2018-12-31 | 2020-07-02 | Lyft, Inc. | Systems and methods for estimating vehicle speed based on radar |
-
2020
- 2020-12-08 CN CN202011422658.3A patent/CN112529955B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001289944A (en) * | 2000-04-06 | 2001-10-19 | Nec Corp | Track tracking method and device therefor |
| CN101718870A (en) * | 2009-11-13 | 2010-06-02 | 西安电子科技大学 | High-speed weak target flight path detection method of image field |
| WO2012136494A1 (en) * | 2011-04-02 | 2012-10-11 | Valeo Schalter Und Sensoren Gmbh | Method for determining a correction value for the measurement of a target angle with a radar device, driver assistance system and motor vehicle |
| CN106054174A (en) * | 2015-04-06 | 2016-10-26 | 通用汽车环球科技运作有限责任公司 | Fusion method for cross traffic application using radars and camera |
| CN108909721A (en) * | 2018-04-28 | 2018-11-30 | 南通职业大学 | A kind of vehicle yaw angle calculation method based on millimetre-wave radar |
| CN108957475A (en) * | 2018-06-26 | 2018-12-07 | 东软集团股份有限公司 | A kind of Method for Road Boundary Detection and device |
| CN109002795A (en) * | 2018-07-13 | 2018-12-14 | 清华大学 | Method for detecting lane lines, device and electronic equipment |
| CN109102702A (en) * | 2018-08-24 | 2018-12-28 | 南京理工大学 | Vehicle speed measuring method based on video encoder server and Radar Signal Fusion |
| FR3086066A1 (en) * | 2018-09-13 | 2020-03-20 | Idemia Identity & Security France | METHOD AND DEVICE FOR MONITORING THE OPERATION OF A ROAD RADAR. |
| US20200209402A1 (en) * | 2018-12-31 | 2020-07-02 | Lyft, Inc. | Systems and methods for estimating vehicle speed based on radar |
| CN110210298A (en) * | 2019-04-25 | 2019-09-06 | 南开大学 | A kind of circuitous path information extraction and representation method based on the aerial visual field |
Non-Patent Citations (1)
| Title |
|---|
| 黄赞杰;: "基于离散点迹的直线运动目标径向速度分析研究", 科技视界, no. 23 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112529955B (en) | 2023-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP4044117A1 (en) | Target tracking method and apparatus, electronic device, and computer-readable storage medium | |
| US11157737B2 (en) | Cultivated land recognition method in satellite image and computing device | |
| CN112507949A (en) | Target tracking method and device, road side equipment and cloud control platform | |
| CN111292531B (en) | Tracking method, device and equipment of traffic signal lamp and storage medium | |
| CN112129266B (en) | Method, apparatus, device and computer readable storage medium for processing map | |
| CN111931704A (en) | Method, device, equipment and computer readable storage medium for evaluating map quality | |
| CN109447074A (en) | A kind of licence plate recognition method and terminal device | |
| Khamael et al. | Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours | |
| Wu et al. | Traffic monitoring and vehicle tracking using roadside cameras | |
| CN113673281B (en) | Speed limit information determining method, device, equipment and storage medium | |
| CN111582317A (en) | Road positioning method and device | |
| CN115690475A (en) | Target detection method and device, electronic equipment and readable storage medium | |
| JP2021043146A (en) | Obstacle detection system and obstacle detection method | |
| CN112529955B (en) | Road normalization and speed recovery method and device for expressway | |
| CN114581890B (en) | Method and device for determining lane line, electronic equipment and storage medium | |
| CN112416128B (en) | Gesture recognition method and terminal equipment | |
| CN115330841A (en) | Method, apparatus, device and medium for detecting projectile based on radar map | |
| CN115267782A (en) | Dangerous area early warning method, device, equipment and medium based on microwave radar | |
| CN112507957B (en) | Vehicle association method and device, road side equipment and cloud control platform | |
| Yi et al. | A clustering-based algorithm for automatic detection of automobile dashboard | |
| CN109583511B (en) | Speed fusion method and device | |
| Wang et al. | Nonlinear circumference-based robust ellipse detection in low-SNR images | |
| CN116778232B (en) | SAR image vertical stripe detection method, device, equipment and storage medium | |
| JP2022136366A (en) | Approach detection device, approach detection method, and program | |
| CN112630770A (en) | Method and device for evaluating service quality of expressway |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |