CN108805932A - A kind of method that high-precision intelligent judges vehicle characteristics point geographic location - Google Patents
A kind of method that high-precision intelligent judges vehicle characteristics point geographic location Download PDFInfo
- Publication number
- CN108805932A CN108805932A CN201810594771.6A CN201810594771A CN108805932A CN 108805932 A CN108805932 A CN 108805932A CN 201810594771 A CN201810594771 A CN 201810594771A CN 108805932 A CN108805932 A CN 108805932A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- target vehicle
- relational model
- models
- model
- 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.)
- Pending
Links
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
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
- G09B19/16—Control of vehicles or other craft
- G09B19/167—Control of land vehicles
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of methods that high-precision intelligent judges vehicle characteristics point geographic location, including：3D scans target vehicle and horizontal line, establishes the first relational model between target vehicle 3D models and horizontal line；From target vehicle 3D models roof downwards and be parallel to horizontal plane direction scan the first relational model, obtain the second relational model between target vehicle wheel touchdown point and horizontal line；Judge whether target vehicle direction is correct in the first relational model；Judge vehicle towards it is correct when, according to the second relational model judge target vehicle whether crimping.
Description
Technical field
The present invention relates to technical field of data processing more particularly to a kind of high-precision intelligent to judge vehicle characteristics point location
The method for managing position.
Background technology
With the continuous development of economic society, vehicle guaranteeding organic quantity maintains sustained and rapid growth, and automobile has become the generation of ordinary people
Step tool, participates in vehicle driver training and examination obtains the demand cumulative year after year of driver's license.
More and more people learn to drive in order to take driving license in driving school, for a long time, study of the driving school for student
Process monitoring takes artificial examination patterns more, not only inefficiency, but also easily makes a fault, and subjective judgement is serious, is unfavorable for learning
The smooth raising for practising student's driving technology in driving procedure, inconvenience is brought to student.
Invention content
Technical problems based on background technology, the present invention propose a kind of high-precision intelligent judge vehicle characteristics point institute
Method in geographical location；
The method that a kind of high-precision intelligent proposed by the present invention judges vehicle characteristics point geographic location, including：
S1,3D scan target vehicle and horizontal line, establish the first relationship mould between target vehicle 3D models and horizontal line
Type；
S2, from target vehicle 3D models roof downwards and be parallel to horizontal plane direction scan the first relational model, obtain mesh
Mark the second relational model between wheel of vehicle touchdown point and horizontal line；
S3, judge whether target vehicle direction is correct in the first relational model；
S4, judge vehicle towards it is correct when, according to the second relational model judge target vehicle whether crimping.
Preferably, step S3 is specifically included：
Target vehicle 3D models in first relational model are correctly compared towards vehicle 3D models with preset, when
In one relational model target vehicle 3D models with it is preset correctly towards vehicle 3D Model Matchings when, judge in the first relational model
Target vehicle is towards correctly.
Preferably, step S4 is specifically included：
Judge vehicle towards it is correct when, judge whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy,
When judging result is no, target vehicle crimping is determined.
A kind of system that high-precision intelligent judges vehicle characteristics point geographic location, including：
First model building module establishes target vehicle 3D models and level for 3D scanning target vehicles and horizontal line
The first relational model between line；
Second model building module, for downwards and being parallel to horizontal plane direction scanning the from target vehicle 3D models roof
One relational model obtains the second relational model between target vehicle wheel touchdown point and horizontal line；
First judgment module, for judging whether target vehicle direction is correct in the first relational model；
Second judgment module, for judge vehicle towards it is correct when, judge that target vehicle is according to the second relational model
No crimping.
Preferably, the first judgment module is specifically used for：
Target vehicle 3D models in first relational model are correctly compared towards vehicle 3D models with preset, when
In one relational model target vehicle 3D models with it is preset correctly towards vehicle 3D Model Matchings when, judge in the first relational model
Target vehicle is towards correctly.
Preferably, the second judgment module is specifically used for：
Judge vehicle towards it is correct when, judge whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy,
When judging result is no, target vehicle crimping is determined.
In the present invention, target vehicle and horizontal line are scanned by 3D, established between target vehicle 3D models and horizontal line
First relational model, to determine the correspondence between target vehicle and horizontal line, then it is downward from target vehicle 3D model roofs
And be parallel to horizontal plane direction and scan the first relational model, second obtained between target vehicle wheel touchdown point and horizontal line is closed
It is model, to obtain correspondence between wheel and horizontal line, when determining that target vehicle headstock is in the right direction, judges target
Wheel of vehicle touchdown point whether be respectively positioned on horizontal homonymy with determine target vehicle whether crimping, to automatically to drive target
The student of vehicle whether assess by correct operation vehicle, and intelligence carries out driving monitoring to student's learning process.
Description of the drawings
Fig. 1 is the flow for the method that a kind of high-precision intelligent proposed by the present invention judges vehicle characteristics point geographic location
Schematic diagram；
Fig. 2 is the module for the system that a kind of high-precision intelligent proposed by the present invention judges vehicle characteristics point geographic location
Schematic diagram.
Specific implementation mode
Referring to Fig.1, the method that a kind of high-precision intelligent proposed by the present invention judges vehicle characteristics point geographic location, packet
It includes：
Step S1,3D scan target vehicle and horizontal line, and first established between target vehicle 3D models and horizontal line is closed
It is model.
Step S2, from target vehicle 3D models roof downwards and be parallel to horizontal plane direction scan the first relational model, obtain
To the second relational model between target vehicle wheel touchdown point and horizontal line.
In concrete scheme, by being scanned simultaneously to target vehicle, target vehicle 3D models and target can be obtained
Position correspondence between vehicle 3D models and horizontal line, is denoted as the first relational model.
From target vehicle 3D models roof downwards and be parallel to horizontal plane direction scan the first relational model, i.e., from target carriage
The roof of 3D models carries out horizontal section to target vehicle 3D models downwards, until target vehicle wheel stops when being contacted with ground
Only, the position correspondence between target vehicle wheel touchdown point and horizontal line is obtained, the second relational model is denoted as.
Step S3 judges that whether target vehicle direction is correct in the first relational model, specifically includes：By the first relational model
Middle target vehicle 3D models are correctly compared towards vehicle 3D models with preset, as target vehicle 3D in the first relational model
Model with it is preset correctly towards vehicle 3D Model Matchings when, judge in the first relational model target vehicle towards correctly.
In concrete scheme, target vehicle direction in the first relational model, and target carriage in the first relational model are extracted
Direction, towards being compared, when such a match occurs, illustrates target vehicle in the first relational model with preset correct vehicle
Towards correctly.
Step S4, judge vehicle towards it is correct when, according to the second relational model judge target vehicle whether crimping, specifically
Including：Judge vehicle towards it is correct when, judge whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy, judging
When being as a result no, target vehicle crimping is determined.
In concrete scheme, judge vehicle towards it is correct when, landed according to target vehicle wheel in the second relational model
Position correspondence between point and horizontal line, judges whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy,
When judging that target vehicle wheel touchdown point is not respectively positioned on horizontal homonymy, target vehicle crimping is determined.
With reference to Fig. 2, a kind of system that high-precision intelligent judges vehicle characteristics point geographic location proposed by the present invention, packet
It includes：
First model building module establishes target vehicle 3D models and level for 3D scanning target vehicles and horizontal line
The first relational model between line.
Second model building module, for downwards and being parallel to horizontal plane direction scanning the from target vehicle 3D models roof
One relational model obtains the second relational model between target vehicle wheel touchdown point and horizontal line.
In concrete scheme, by being scanned simultaneously to target vehicle, target vehicle 3D models and target can be obtained
Position correspondence between vehicle 3D models and horizontal line, is denoted as the first relational model.
From target vehicle 3D models roof downwards and be parallel to horizontal plane direction scan the first relational model, i.e., from target carriage
The roof of 3D models carries out horizontal section to target vehicle 3D models downwards, until target vehicle wheel stops when being contacted with ground
Only, the position correspondence between target vehicle wheel touchdown point and horizontal line is obtained, the second relational model is denoted as.
First judgment module, for judging that whether target vehicle direction is correct in the first relational model, is specifically used for：By
Target vehicle 3D models are correctly compared towards vehicle 3D models with preset in one relational model, when in the first relational model
Target vehicle 3D models with it is preset correctly towards vehicle 3D Model Matchings when, judge target vehicle direction in the first relational model
Correctly.
In concrete scheme, target vehicle direction in the first relational model, and target carriage in the first relational model are extracted
Direction, towards being compared, when such a match occurs, illustrates target vehicle in the first relational model with preset correct vehicle
Towards correctly.
Second judgment module, for judge vehicle towards it is correct when, judge that target vehicle is according to the second relational model
No crimping, is specifically used for：Judge vehicle towards it is correct when, it is horizontal to judge whether target vehicle wheel touchdown point is respectively positioned on
Homonymy determines target vehicle crimping when the judgment result is No.
In concrete scheme, judge vehicle towards it is correct when, landed according to target vehicle wheel in the second relational model
Position correspondence between point and horizontal line, judges whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy,
When judging that target vehicle wheel touchdown point is not respectively positioned on horizontal homonymy, target vehicle crimping is determined.
In present embodiment, target vehicle and horizontal line are scanned by 3D, establish target vehicle 3D models and horizontal line it
Between the first relational model, to determine the correspondence between target vehicle and horizontal line, then from target vehicle 3D model roofs
It downwards and is parallel to horizontal plane direction and scans the first relational model, obtain the between target vehicle wheel touchdown point and horizontal line
Two relational models, when determining that target vehicle headstock is in the right direction, judge to obtain correspondence between wheel and horizontal line
Target vehicle wheel touchdown point whether be respectively positioned on horizontal homonymy with determine target vehicle whether crimping, to automatically to drive
The student of target vehicle whether assess by correct operation vehicle, and intelligence carries out driving monitoring to student's learning process.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (6)
1. a kind of method that high-precision intelligent judges vehicle characteristics point geographic location, which is characterized in that including：
S1,3D scan target vehicle and horizontal line, establish the first relational model between target vehicle 3D models and horizontal line；
S2, from target vehicle 3D models roof downwards and be parallel to horizontal plane direction scan the first relational model, obtain target carriage
The second relational model between wheel touchdown point and horizontal line；
S3, judge whether target vehicle direction is correct in the first relational model；
S4, judge vehicle towards it is correct when, according to the second relational model judge target vehicle whether crimping.
2. the method that high-precision intelligent according to claim 1 judges vehicle characteristics point geographic location, feature exist
In step S3 is specifically included：
Target vehicle 3D models in first relational model are correctly compared towards vehicle 3D models with preset, when the first pass
Be in model target vehicle 3D models with it is preset correctly towards vehicle 3D Model Matchings when, judge target in the first relational model
Vehicle is towards correctly.
3. the method that high-precision intelligent according to claim 1 judges vehicle characteristics point geographic location, feature exist
In step S4 is specifically included：
Judge vehicle towards it is correct when, judge whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy, judging
When being as a result no, target vehicle crimping is determined.
4. the system that a kind of high-precision intelligent judges vehicle characteristics point geographic location, which is characterized in that including：
First model building module, for 3D scanning target vehicles and horizontal line, establish target vehicle 3D models and horizontal line it
Between the first relational model；
Second model building module, for from target vehicle 3D models roof downwards and be parallel to horizontal plane direction scanning first close
It is model, obtains the second relational model between target vehicle wheel touchdown point and horizontal line；
First judgment module, for judging whether target vehicle direction is correct in the first relational model；
Second judgment module, for judge vehicle towards it is correct when, judge whether target vehicle presses according to the second relational model
Line.
5. the system that high-precision intelligent according to claim 4 judges vehicle characteristics point geographic location, feature exist
In the first judgment module is specifically used for：
Target vehicle 3D models in first relational model are correctly compared towards vehicle 3D models with preset, when the first pass
Be in model target vehicle 3D models with it is preset correctly towards vehicle 3D Model Matchings when, judge target in the first relational model
Vehicle is towards correctly.
6. the system that high-precision intelligent according to claim 4 judges vehicle characteristics point geographic location, feature exist
In the second judgment module is specifically used for：
Judge vehicle towards it is correct when, judge whether target vehicle wheel touchdown point is respectively positioned on horizontal homonymy, judging
When being as a result no, target vehicle crimping is determined.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810594771.6A CN108805932A (en) | 2018-06-11 | 2018-06-11 | A kind of method that high-precision intelligent judges vehicle characteristics point geographic location |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810594771.6A CN108805932A (en) | 2018-06-11 | 2018-06-11 | A kind of method that high-precision intelligent judges vehicle characteristics point geographic location |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108805932A true CN108805932A (en) | 2018-11-13 |
Family
ID=64088361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810594771.6A Pending CN108805932A (en) | 2018-06-11 | 2018-06-11 | A kind of method that high-precision intelligent judges vehicle characteristics point geographic location |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108805932A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287936A (en) * | 2019-07-02 | 2019-09-27 | 北京字节跳动网络技术有限公司 | Image detecting method, device, equipment and storage medium |
| CN110853366A (en) * | 2019-11-20 | 2020-02-28 | 浙江大华技术股份有限公司 | Method and device for detecting parking position of vehicle |
| CN111882882A (en) * | 2020-07-31 | 2020-11-03 | 浙江东鼎电子股份有限公司 | Method for detecting cross-lane driving behavior of automobile in dynamic flat-plate scale weighing area |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011148455A1 (en) * | 2010-05-25 | 2011-12-01 | 富士通株式会社 | Video processing device, video processing method, and video processing program |
| CN102938224A (en) * | 2012-11-06 | 2013-02-20 | 福州龙吟信息技术有限公司 | Driving test intelligent system |
| CN104129389A (en) * | 2014-08-06 | 2014-11-05 | 中电海康集团有限公司 | Method for effectively judging and recognizing vehicle travelling conditions and device thereof |
| US9396659B2 (en) * | 2013-12-12 | 2016-07-19 | Lenovo Enterprise Solutions (Singapore) Pte. Ltd. | Collision avoidance among vehicles |
| CN106373218A (en) * | 2016-10-28 | 2017-02-01 | 中山大学 | Cell phone automobile data recorder with line pressing alarm function |
| CN106559646A (en) * | 2015-09-28 | 2017-04-05 | 柳州桂通科技股份有限公司 | A kind of panoramic optical monitoring system and its collection for Driving Test information and processing method for serving Driving Test vehicle |
| CN106875781A (en) * | 2017-03-16 | 2017-06-20 | 南京多伦科技股份有限公司 | A kind of intelligent robot trains auxiliary driving method and its system |
| CN107292282A (en) * | 2017-07-04 | 2017-10-24 | 西安交通大学 | A kind of vehicle checking method updated based on semantic reasoning and space-time memory |
-
2018
- 2018-06-11 CN CN201810594771.6A patent/CN108805932A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011148455A1 (en) * | 2010-05-25 | 2011-12-01 | 富士通株式会社 | Video processing device, video processing method, and video processing program |
| CN102938224A (en) * | 2012-11-06 | 2013-02-20 | 福州龙吟信息技术有限公司 | Driving test intelligent system |
| US9396659B2 (en) * | 2013-12-12 | 2016-07-19 | Lenovo Enterprise Solutions (Singapore) Pte. Ltd. | Collision avoidance among vehicles |
| CN104129389A (en) * | 2014-08-06 | 2014-11-05 | 中电海康集团有限公司 | Method for effectively judging and recognizing vehicle travelling conditions and device thereof |
| CN106559646A (en) * | 2015-09-28 | 2017-04-05 | 柳州桂通科技股份有限公司 | A kind of panoramic optical monitoring system and its collection for Driving Test information and processing method for serving Driving Test vehicle |
| CN106373218A (en) * | 2016-10-28 | 2017-02-01 | 中山大学 | Cell phone automobile data recorder with line pressing alarm function |
| CN106875781A (en) * | 2017-03-16 | 2017-06-20 | 南京多伦科技股份有限公司 | A kind of intelligent robot trains auxiliary driving method and its system |
| CN107292282A (en) * | 2017-07-04 | 2017-10-24 | 西安交通大学 | A kind of vehicle checking method updated based on semantic reasoning and space-time memory |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287936A (en) * | 2019-07-02 | 2019-09-27 | 北京字节跳动网络技术有限公司 | Image detecting method, device, equipment and storage medium |
| CN110287936B (en) * | 2019-07-02 | 2021-08-10 | 北京字节跳动网络技术有限公司 | Image detection method, device, equipment and storage medium |
| CN110853366A (en) * | 2019-11-20 | 2020-02-28 | 浙江大华技术股份有限公司 | Method and device for detecting parking position of vehicle |
| CN111882882A (en) * | 2020-07-31 | 2020-11-03 | 浙江东鼎电子股份有限公司 | Method for detecting cross-lane driving behavior of automobile in dynamic flat-plate scale weighing area |
| CN111882882B (en) * | 2020-07-31 | 2021-06-25 | 浙江东鼎电子股份有限公司 | Method for detecting cross-lane driving behavior of automobile in dynamic flat-plate scale weighing area |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108388641B (en) | Traffic facility map generation method and system based on deep learning | |
| CN108805932A (en) | A kind of method that high-precision intelligent judges vehicle characteristics point geographic location | |
| CN104166400B (en) | Multi-sensor fusion-based visual navigation AGV system | |
| CN103226106B (en) | Based on the bearing dustproof cover quality monitoring method of machine vision | |
| CN103577809B (en) | A kind of method that traffic above-ground mark based on intelligent driving detects in real time | |
| CN103714343B (en) | Under laser line generator lighting condition, the pavement image of twin-line array collected by camera splices and homogenizing method | |
| CN103359020B (en) | A kind of motor vehicle driving training or examination method for supervising and system | |
| CN103136341B (en) | A kind of lane line based on Bézier curve reconstruct device | |
| CN108537197A (en) | A kind of lane detection prior-warning device and method for early warning based on deep learning | |
| CN107264486A (en) | Vision positioning changes method for electrically and system | |
| CN102663403B (en) | System and method used for extracting lane information in express way intelligent vehicle-navigation and based on vision | |
| CN107016362A (en) | Vehicle based on vehicle front windshield sticking sign recognition methods and system again | |
| CN107578012A (en) | A kind of drive assist system based on clustering algorithm selection sensitizing range | |
| KR102025132B1 (en) | Tire pressure measurement system and method based on image analysis | |
| CN110516633A (en) | A kind of method for detecting lane lines and system based on deep learning | |
| CN103481842B (en) | A kind of changing method of moving vehicles detection and tracking pattern | |
| CN101609555A (en) | A kind of gray-scale template matching method based on gray level co-occurrence matrixes | |
| CN105891220A (en) | Pavement marker line defect detecting device and detecting method thereof | |
| CN109190483A (en) | A kind of method for detecting lane lines of view-based access control model | |
| CN110599025A (en) | Method for evaluating reliability index of driving behavior of automatic driving automobile | |
| CN108074263B (en) | Visual positioning method and system | |
| CN111046809B (en) | Obstacle detection method, device, equipment and computer readable storage medium | |
| CN114550334A (en) | Bridge robot inspection teaching training system, method and storage medium | |
| CN111091552A (en) | Method for identifying closing fault image of angle cock handle of railway wagon | |
| CN104268884A (en) | Lane departure early warning calibration system and method based on vehicle networking |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181113 |