CN108389392A - A kind of traffic accident responsibility identification system based on machine learning - Google Patents
A kind of traffic accident responsibility identification system based on machine learning Download PDFInfo
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- 206010039203 Road traffic accident Diseases 0.000 title claims abstract description 48
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- 238000004458 analytical method Methods 0.000 claims abstract description 44
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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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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Abstract
The invention discloses a kind of, and the traffic accident responsibility based on machine learning assert system,It is related to traffic accident responsibility and assert technical field,Including traffic information interaction center,Image processing system and accident responsibility analysis system,The traffic information interaction center includes pictograph receiving module and identification result sending module,Described image processing system includes image load-on module,Color extraction module,Profile extraction module,Gaussian Blur module and distance analysis module,The accident responsibility analysis system includes feature case searching module and comparing calculation module,The present invention replaces traffic police personnel to carry out take pictures evidence obtaining and the responsibility analysis judgement of back information progress in real time to the scene of a traffic accident by reporter,Effectively reduce the time of in-site measurement forensics analysis judgement,It is efficient,Accuracy of judgement,Reduce artificial subjective factor,And traffic can be made to restore as early as possible,It avoids the jams.
Description
Technical field
The present invention relates to traffic accident responsibilities to assert technical field, and in particular to a kind of traffic accident based on machine learning
Confirmation of responsibility system and its confirmation of responsibility method.
Background technology
As people’s lives level is continuously improved, the ownership of motor vehicle is continuously increased.The peace of some driver
Full consciousness is not strong, and road traffic accident happens occasionally.After traffic accident occurs, it is usually associated with traffic congestion etc. and is attached to traffic and ask
Topic causes traffic police to tend not to carry out confirmation of responsibility to scene of the accident forensics analysis in time.It, be to thing after traffic police reaches scene
Therefore section closes a road to traffic and carries out hand dipping to the floor line of the scene of the accident and the braking mark of motor vehicle to facilitate, then
To accident occur responsibility judge, if accident occur section it is complex, in-site measurement evidence obtaining may spend compared with
The long time, and since the drawing level of traffic police differs, can not standardize, it is also possible to it can cause to take longer time to paint
System.The hand dipping time is longer, and the influence to road traffic is bigger, it is most likely that causes second accident.
China Patent Publication No. is judgement system and the judgement side that CN104574882A discloses that traffic accident occurs for vehicle
Method is mainly monitored the traffic accident of generation by being installed on the car-mounted terminal of vehicle, and car-mounted terminal is mainly defended including GPS
Star module, gsm module, 3D power accelerations inductor and CPU processor, it is accurately fixed that GPS satellite module is used to carry out vehicle
Position, gsm module are used for transmission voice messaging, and 3D power acceleration inductors are used to judge whether vehicle collides or turn on one's side,
CPU processor is for handling data, and the scientific and technological ingredient of the car-mounted terminal is higher, although sentencing for traffic accident can be realized
It is disconnected, but be only capable of working to an automobile, the purchase cost of user is increased, the reasonable utilization of resource is unfavorable for.
China Patent Publication No. is that CN107067718A discloses traffic accident responsibility appraisal procedure, traffic accident responsibility is commented
Estimate device and traffic accident responsibility assessment system, main application is in automobile data recorder, including obtains video pictures and row
Vehicle information analyzes the traffic accident in video pictures in conjunction with running information, shows analysis result using the responsibility as traffic accident
The foundation of assessment, but its carrier is automobile data recorder, it is impossible to comprehensive acquisition accident specifying information, there are certain to lack
It falls into.
Invention content
The purpose of the present invention is to provide a kind of, and the traffic accident responsibility based on machine learning assert system, described in solution
The defect that content proposes.
A kind of traffic accident responsibility identification system based on machine learning, including traffic information interaction center, image procossing
System and accident responsibility analysis system, the traffic information interaction center are electrically connected with described image processing system, the figure
As processing system and the accident responsibility analysis system are electrically connected；
The traffic information interaction center includes pictograph receiving module and assert that result sending module, described image are literary
Word receiving module receives the picture of scene of the accident transmission, the final result assert result sending module and send system judgement；
Described image processing system includes image load-on module, color extraction module, profile extraction module, Gaussian Blur mould
Block and distance analysis module, the image data of described image load-on module load image received text module conveying, the face
Color extraction module extract vehicle body, braking mark, floor line information, the profile extraction module extract vehicle body, braking mark,
Floor line profile information, the Gaussian Blur module carry out noise reduction, the distance to the graph data after extraction profile
Analysis module establishes coordinate system to the image information after noise reduction, calculates the relative distance of vehicle body and floor line, and brake
The characteristic parameters such as distance；
The accident responsibility analysis system includes feature case searching module and comparing calculation module, the feature case
Searching module contrast characteristic parameter carries out lookup comparison, the comparing calculation module in the traffic accident typical case of storage
Accident responsibility judgement is carried out to the liability cause that accident occurs using KNN algorithms according to lookup result.
Preferably, described image received text module can bi-directional data.
Preferably, traffic accident typical case's case of traffic control center processing is stored in the feature case searching module
Example.
A kind of traffic accident responsibility identification based on machine learning, includes the following steps：
(1) it after traffic accident party reports a case to the security authorities, is taken pictures using the mobile device reported a case to the security authorities and sends best friend and led in information exchange
The pictograph information received is transferred to image procossing system by the pictograph receiving module of the heart, traffic information interaction center
System；
(2) it after image processing system receives the pictograph information that traffic information interaction center is transmitted, is loaded by image
Vehicle body, braking mark, floor line etc. are extracted by color extraction module, pass through profile by module loading image data
Extraction module comes out the contours extract of vehicle body, braking mark, floor line etc., by Gaussian Blur module to extraction profile it
Graph data afterwards carries out noise reduction, establishes coordinate system to the image information after noise reduction by distance analysis module, calculates vehicle body
Accident responsibility analysis system is transferred to the characteristic parameters such as the relative distance of floor line and braking distance and by result of calculation
System；
(3) after accident responsibility analysis system receives the characteristic parameter that image processing system transmits, feature case searches mould
Block contrast characteristic parameter carries out lookup comparison in the traffic accident typical case of storage, and comparing calculation module is according to lookup result
Accident responsibility judgement is carried out to the liability cause that accident occurs using KNN algorithms, result and judgment basis will be judged into line number
It is sent to traffic information interaction center according to deposit database of putting on record, and by last judgement result；
(4) after traffic information interaction center receives the judgement result that accident responsibility analysis system is transmitted, assert result hair
Send module that casualty effect is sent to the mobile device reported a case to the security authorities according to specific format.
Preferably, image processing system is in processing image, when calculating characteristic parameter, if it find that there is the something lost of important picture
Leakage, can back transfer message to traffic information interaction center, and by pictograph receiving module back transfer to reporter carry out
Retake.
The advantage of the invention is that：By traffic accident party carry out scene take pictures evidence obtaining and real-time Transmission, can be effective
The time that manual site measures evidence obtaining is reduced, traffic can be made to restore as early as possible, avoided the jams, be compared, calculated by system
Final judgement is obtained with analysis as a result, reducing the influence of artificial subjective factor relative to artificial judgement, and more efficient, sentence
It is disconnected accurate.
Description of the drawings
Fig. 1 is that the traffic accident responsibility based on machine learning assert that the principle of system and traffic accident responsibility are recognized in the present invention
Determine method flow diagram.
Specific implementation mode
To make the technical means, the creative features, the aims and the efficiencies achieved by the present invention be easy to understand, with reference to
Specific implementation mode, the present invention is further explained.
Embodiment 1
As shown in Figure 1, a kind of traffic accident responsibility based on machine learning is assert in system, including traffic information interaction
The heart, image processing system and accident responsibility analysis system, the traffic information interaction center and described image processing system are electrical
Connection, described image processing system are electrically connected with the accident responsibility analysis system；
The traffic information interaction center includes pictograph receiving module and assert that result sending module, described image are literary
Word receiving module receives the picture of scene of the accident transmission, the final result assert result sending module and send system judgement；
Described image processing system includes image load-on module, color extraction module, profile extraction module, Gaussian Blur mould
Block and distance analysis module, the image data of described image load-on module load image received text module conveying, the face
Color extraction module extract vehicle body, braking mark, floor line information, the profile extraction module extract vehicle body, braking mark,
Floor line profile information, the Gaussian Blur module carry out noise reduction, the distance to the graph data after extraction profile
Analysis module establishes coordinate system to the image information after noise reduction, calculates the relative distance of vehicle body and floor line, and brake
The characteristic parameters such as distance；
The accident responsibility analysis system includes feature case searching module and comparing calculation module, the feature case
Searching module contrast characteristic parameter carries out lookup comparison, the comparing calculation module in the traffic accident typical case of storage
Accident responsibility judgement is carried out to the liability cause that accident occurs using KNN algorithms according to lookup result.
In the present embodiment, the broadcasting adjustment module supports mouse, keyboard dragging.
In the present embodiment, the traffic accident allusion quotation of traffic control center processing is stored in the feature case searching module
Type case.
A kind of traffic accident responsibility identification based on machine learning, includes the following steps：
(1) it after traffic accident party reports a case to the security authorities, is taken pictures using the mobile device reported a case to the security authorities and sends best friend and led in information exchange
The pictograph information received is transferred to image procossing system by the pictograph receiving module of the heart, traffic information interaction center
System；
(2) it after image processing system receives the pictograph information that traffic information interaction center is transmitted, is loaded by image
Vehicle body, braking mark, floor line etc. are extracted by color extraction module, pass through profile by module loading image data
Extraction module comes out the contours extract of vehicle body, braking mark, floor line etc., by Gaussian Blur module to extraction profile it
Graph data afterwards carries out noise reduction, establishes coordinate system to the image information after noise reduction by distance analysis module, calculates vehicle body
Accident responsibility analysis system is transferred to the characteristic parameters such as the relative distance of floor line and braking distance and by result of calculation
System；
(3) after accident responsibility analysis system receives the characteristic parameter that image processing system transmits, feature case searches mould
Block contrast characteristic parameter carries out lookup comparison in the traffic accident typical case of storage, and comparing calculation module is according to lookup result
Accident responsibility judgement is carried out to the liability cause that accident occurs using KNN algorithms, result and judgment basis will be judged into line number
It is sent to traffic information interaction center according to deposit database of putting on record, and by last judgement result；
(4) after traffic information interaction center receives the judgement result that accident responsibility analysis system is transmitted, assert result hair
Send module that casualty effect is sent to the mobile device reported a case to the security authorities according to specific format.
Embodiment 2
As shown in Figure 1, a kind of traffic accident responsibility based on machine learning is assert in system, including traffic information interaction
The heart, image processing system and accident responsibility analysis system, the traffic information interaction center and described image processing system are electrical
Connection, described image processing system are electrically connected with the accident responsibility analysis system；
The traffic information interaction center includes pictograph receiving module and assert that result sending module, described image are literary
Word receiving module receives the picture of scene of the accident transmission, the final result assert result sending module and send system judgement；
Described image processing system includes image load-on module, color extraction module, profile extraction module, Gaussian Blur mould
Block and distance analysis module, the image data of described image load-on module load image received text module conveying, the face
Color extraction module extract vehicle body, braking mark, floor line information, the profile extraction module extract vehicle body, braking mark,
Floor line profile information, the Gaussian Blur module carry out noise reduction, the distance to the graph data after extraction profile
Analysis module establishes coordinate system to the image information after noise reduction, calculates the relative distance of vehicle body and floor line, and brake
The characteristic parameters such as distance；
The accident responsibility analysis system includes feature case searching module and comparing calculation module, the feature case
Searching module contrast characteristic parameter carries out lookup comparison, the comparing calculation module in the traffic accident typical case of storage
Accident responsibility judgement is carried out to the liability cause that accident occurs using KNN algorithms according to lookup result.
In the present embodiment, the broadcasting adjustment module supports mouse, keyboard dragging.
In the present embodiment, the traffic accident allusion quotation of traffic control center processing is stored in the feature case searching module
Type case.
A kind of traffic accident responsibility identification based on machine learning, includes the following steps：
(1) it after traffic accident party reports a case to the security authorities, is taken pictures using the mobile device reported a case to the security authorities and sends best friend and led in information exchange
The pictograph information received is transferred to image procossing system by the pictograph receiving module of the heart, traffic information interaction center
System；
(2) it after image processing system receives the pictograph information that traffic information interaction center is transmitted, is loaded by image
Vehicle body, braking mark, floor line etc. are extracted by color extraction module, pass through profile by module loading image data
Extraction module comes out the contours extract of vehicle body, braking mark, floor line etc., by Gaussian Blur module to extraction profile it
Graph data afterwards carries out noise reduction, establishes coordinate system to the image information after noise reduction by distance analysis module, calculates vehicle body
Accident responsibility analysis system is transferred to the characteristic parameters such as the relative distance of floor line and braking distance and by result of calculation
System；
(3) after accident responsibility analysis system receives the characteristic parameter that image processing system transmits, feature case searches mould
Block contrast characteristic parameter carries out lookup comparison in the traffic accident typical case of storage, and comparing calculation module is according to lookup result
Accident responsibility judgement is carried out to the liability cause that accident occurs using KNN algorithms, result and judgment basis will be judged into line number
It is sent to traffic information interaction center according to deposit database of putting on record, and by last judgement result；
(4) after traffic information interaction center receives the judgement result that accident responsibility analysis system is transmitted, assert result hair
Send module that casualty effect is sent to the mobile device reported a case to the security authorities according to specific format.
In the present embodiment, image processing system is in processing image, when calculating characteristic parameter, if it find that there is important picture
Omission, can back transfer message to traffic information interaction center, and by pictograph receiving module back transfer to reporter
Carry out retake.
Based on described, the present invention carries out scene by mobile device and takes pictures evidence obtaining and real-time Transmission, can effectively reduce artificial
The time of in-site measurement evidence obtaining, traffic can be made to restore as early as possible, avoided the jams, be compared, calculated and analyzed by system
Go out final judgement as a result, reducing the influence of artificial subjective factor relative to artificial judgement, and more efficient, accuracy of judgement.
As known by the technical knowledge, the present invention can pass through the embodiment party of other essence without departing from its spirit or essential feature
Case is realized.Therefore, the disclosed embodiment, all things considered are all merely illustrative, not the only.Institute
Have within the scope of the present invention or is included in the invention in the change being equal in the scope of the present invention.
Claims (5)
1. a kind of traffic accident responsibility based on machine learning assert system, it is characterised in that：Including traffic information interaction center,
Image processing system and accident responsibility analysis system, the traffic information interaction center electrically connect with described image processing system
It connects, described image processing system is electrically connected with the accident responsibility analysis system；
The traffic information interaction center includes pictograph receiving module and assert that result sending module, described image word connect
Receive the picture that module receives scene of the accident transmission, the final result assert result sending module and send system judgement；
Described image processing system include image load-on module, color extraction module, profile extraction module, Gaussian Blur module and
Distance analysis module, the image data of described image load-on module load image received text module conveying, the color carry
Modulus block extracts vehicle body, braking mark, floor line information, and the profile extraction module extracts vehicle body, braking mark, ground
The profile information of graticule, the Gaussian Blur module carry out noise reduction, the distance point to the graph data after extraction profile
Analysis module establishes coordinate system to the image information after noise reduction and calculates characteristic parameter, and the characteristic parameter includes vehicle body and ground
The relative distance and braking distance of graticule；
The accident responsibility analysis system includes feature case searching module and comparing calculation module, and the feature case is searched
Module contrast characteristic parameter carries out lookup comparison in the traffic accident typical case of storage, the comparing calculation module according to
Lookup result carries out accident responsibility judgement using KNN algorithms to the liability cause that accident occurs.
2. a kind of traffic accident responsibility based on machine learning according to claim 1 assert system, it is characterised in that：Institute
Stating pictograph receiving module can bi-directional data.
3. a kind of traffic accident responsibility based on machine learning according to claim 1 assert system, it is characterised in that：Institute
State the traffic accident typical case that traffic control center processing is stored in feature case searching module.
4. a kind of traffic accident responsibility identification according to claim 3 based on machine learning, which is characterized in that packet
Include following steps：
(1) after traffic accident party reports a case to the security authorities, being taken pictures using the mobile device reported a case to the security authorities and sending best friend communicates breath interaction center, hands over
The pictograph information received is transferred to image processing system by the pictograph receiving module of communication breath interaction center；
(2) after image processing system receives the pictograph information that traffic information interaction center is transmitted, by image load-on module
Load Image data, extracts vehicle body, braking mark, floor line information by color extraction module, is carried by profile
Modulus block extracts the profile information of vehicle body, braking mark, floor line, by Gaussian Blur module to extraction profile it
Graph data afterwards carries out noise reduction, establishes coordinate system to the image information after noise reduction by distance analysis module, calculates feature
Parameter, including the relative distance and braking distance of vehicle body and floor line and by result of calculation be transferred to accident responsibility analysis system
System；
(3) after accident responsibility analysis system receives the characteristic parameter that image processing system transmits, feature case searching module pair
Lookup comparison is carried out in the traffic accident typical case of storage than characteristic parameter, comparing calculation module is utilized according to lookup result
KNN algorithms carry out accident responsibility judgement to the liability cause that accident occurs, and it is standby that judgement result and judgment basis are carried out data
Case is stored in database, and last judgement result is sent to traffic information interaction center；
(4) after traffic information interaction center receives the judgement result that accident responsibility analysis system is transmitted, assert that result sends mould
Casualty effect is sent to the mobile device reported a case to the security authorities by block according to specific format.
5. a kind of traffic accident responsibility identification based on machine learning according to claim 4, it is characterised in that：Figure
As processing system is in processing image, when calculating characteristic parameter, if it find that there is the omission of important picture, can back transfer message extremely
Traffic information interaction center, and retake is carried out by pictograph receiving module back transfer to reporter.
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| CN109961056A (en) * | 2019-04-02 | 2019-07-02 | 浙江科技学院 | Method, system and equipment for determining responsibility for traffic accident based on decision tree algorithm |
| CN110135418A (en) * | 2019-04-15 | 2019-08-16 | 深圳壹账通智能科技有限公司 | Traffic accident fix duty method, apparatus, equipment and storage medium based on picture |
| CN111369397A (en) * | 2020-02-29 | 2020-07-03 | 重庆百事得大牛机器人有限公司 | Evidence collection suggestion generation system and method for legal consultation |
| CN111696015A (en) * | 2019-03-13 | 2020-09-22 | 阿里巴巴集团控股有限公司 | Accident processing method, device and system, computing equipment and storage medium |
| CN111754211A (en) * | 2020-07-27 | 2020-10-09 | 杭州钧钥信息科技有限公司 | An automatic generation system of intelligent analysis report based on big data |
| CN112233421A (en) * | 2020-10-15 | 2021-01-15 | 胡歆柯 | Intelligent city intelligent traffic monitoring system based on machine vision |
| CN113643520A (en) * | 2021-08-04 | 2021-11-12 | 南京及物智能技术有限公司 | Intelligent traffic accident processing system and method |
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| CN111696015A (en) * | 2019-03-13 | 2020-09-22 | 阿里巴巴集团控股有限公司 | Accident processing method, device and system, computing equipment and storage medium |
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Application publication date: 20180810 |