CN104504384B - Automobile logo identification method and its identifying system - Google Patents
Automobile logo identification method and its identifying system Download PDFInfo
- Publication number
- CN104504384B CN104504384B CN201510021400.5A CN201510021400A CN104504384B CN 104504384 B CN104504384 B CN 104504384B CN 201510021400 A CN201510021400 A CN 201510021400A CN 104504384 B CN104504384 B CN 104504384B
- Authority
- CN
- China
- Prior art keywords
- logo
- vehicle
- major class
- recognition region
- hog
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/625—License plates
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/09—Recognition of logos
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
- Traffic Control Systems (AREA)
Abstract
A kind of automobile logo identification method, includes the following steps：Picture pre-treatment step, vehicle-logo recognition region coarse positioning step and vehicle-logo recognition step.The present invention has the following advantages：Identification is accurate, according to identification demand can identify logo major class, logo major class, subclass and the time limit at logo major class and subclass or identification at identification；Identification is comprehensive, can realize the vehicle-logo recognition of preceding board and realize the vehicle-logo recognition of tail board.
Description
Technical field
The invention belongs to vehicle-logo recognition technical field more particularly to a kind of automobile logo identification methods and its identifying system.
Background technology
Vehicle-logo recognition technology is an important content of intelligent transport technology research field, the research of the technology, for vehicle
Control, highway toll, the highway contents such as deploy to ensure effective monitoring and control of illegal activities have far-reaching significance.The research of existing vehicle-logo recognition technology is mainly concentrated
In：The positioning of logo and identification two parts content of logo.In the method for vehicle-logo location, it common are：Based on edge histogram
The quick automobile logo identification method of figure, the vehicle-logo location method based on energy feature, the vehicle based on energy enhancing and morphologic filtering
Localization method etc. is marked, and the identification technology of logo is then more based on basis of classification such as PCA or Adaboost.And above-mentioned logo
Identification technology, the other identification of board vehicle logo major class before most only single progress.However, in actual traffic control, it is right
In the acquisition of information of vehicle, it is more desirable to the vehicle-logo recognition that can realize preceding board vehicle and the vehicle-logo recognition for realizing tail board vehicle.
Even, (such as some specific demands：The escape vehicle that quickly positioning suspect drives), the logo obtained is also needed to sometimes
Information can be accurate to the model even time limit of logo.Therefore, comprehensive, accurate vehicle-logo recognition technology, Shi Bicheng how to be realized
For the development trend of intelligent transportation field vehicle-logo recognition technology.
Invention content
Based on this, in view of the above technical problems, a kind of automobile logo identification method and its identifying system are provided.
In order to solve the above technical problems, the present invention adopts the following technical scheme that：
A kind of automobile logo identification method, includes the following steps：
Picture pre-processes：Binary conversion treatment is carried out to the video pictures comprising vehicle, obtains bianry image；
Vehicle-logo recognition region coarse positioning：Sobel edge detections are carried out to the bianry image, obtain including vehicle target area
g′(x,y)；In the region g ' (x, y), the x for meeting formula (1) is chosenmin,xmax,ymin,ymax：
α is taken, the value of β is 1, and passes through formula (2) coarse positioning vehicle-logo recognition region (x, y, α W, β H)：
W is the width in vehicle-logo recognition region, and H is the height in vehicle-logo recognition region, and α, β are respectively width, height parameter,
(x, y) is the coordinate of each pixel in vehicle-logo recognition region；
Vehicle-logo recognition：When identifying logo major class, HOG and PCA is carried out in the vehicle-logo recognition region of α=0.2, β=0.2
The extraction of feature identifies logo major class by logo grader；When identifying logo major class and subclass, in α=1.3, β=0.2
Vehicle-logo recognition region carry out the extractions of HOG and PCA features, pass through logo grader and identify logo major class and subclass；Know
It other logo major class, subclass and prescribes a time limit in year, carrying for HOG and PCA features is carried out in the vehicle-logo recognition region of α=1.3, β=0.6
It takes, logo major class, subclass and the time limit is identified by logo grader.
The logo grader is trained by following steps and is obtained：
Logo samples pictures are pre-processed by the picture pre-treatment step；
Vehicle-logo recognition region is positioned in the logo samples pictures by the vehicle-logo recognition region coarse positioning step；
The extraction of HOG and PCA features is carried out in the vehicle-logo recognition region；
The logo grader is trained with HOG the and PCA features.
This programme further relates to a kind of vehicle-logo recognition system, including：
Picture pretreatment unit obtains bianry image for carrying out binary conversion treatment to the video pictures comprising vehicle；
Vehicle-logo recognition region coarse positioning unit obtains including vehicle for carrying out sobel edge detections to the bianry image
Target area g ' (x, y)；In the region g ' (x, y), the x for meeting formula (1) is chosenmin,xmax,ymin,ymax：
α is taken, the value of β is 1, and passes through formula (2) coarse positioning vehicle-logo recognition region (x, y, α W, β H)：
W is the width in vehicle-logo recognition region, and H is the height in vehicle-logo recognition region, and α, β are respectively width, height parameter,
(x, y) is the coordinate of each pixel in vehicle-logo recognition region；
For identification when logo major class, HOG is carried out in the vehicle-logo recognition region of α=0.2, β=0.2 for vehicle-logo recognition unit
And the extraction of PCA features, logo major class is identified by logo grader；Identify logo major class and when subclass, in α=1.3,
The vehicle-logo recognition region of β=0.2 carries out the extraction of HOG and PCA features, and logo major class and son are identified by logo grader
Class；It identifies logo major class, subclass and prescribes a time limit in year, HOG and PCA spies are carried out in the vehicle-logo recognition region of α=1.3, β=0.6
The extraction of sign identifies logo major class, subclass and the time limit by logo grader.
The logo grader is trained by following steps and is obtained：
Logo samples pictures are pre-processed by the picture pre-treatment step；
Vehicle-logo recognition region is positioned in the logo samples pictures by the vehicle-logo recognition region coarse positioning step；
The extraction of HOG and PCA features is carried out in the vehicle-logo recognition region；
The logo grader is trained with HOG the and PCA features.
The present invention has the following advantages：
1, identification is accurate：Logo major class can be identified according to identification demand, logo major class and subclass at identification, or
Logo major class, subclass and the time limit at person's identification；
2, identification is comprehensive：It can realize the vehicle-logo recognition of preceding board and realize the vehicle-logo recognition of tail board.
Description of the drawings
The present invention is described in detail with reference to the accompanying drawings and detailed description：
Fig. 1 is a kind of flow chart of automobile logo identification method of the present invention；
Fig. 2 is a kind of structural schematic diagram of vehicle-logo recognition system of the present invention.
Specific implementation mode
As shown in Figure 1, a kind of automobile logo identification method, includes the following steps：
S110, picture pretreatment：Priori is used for reference, logo is normally above the level of car plate and logo is general
In the vertical axis of symmetry of the front/rear face of vehicle, binary conversion treatment is carried out to the video pictures comprising vehicle, obtains bianry image
g(x,y)；
S120, vehicle-logo recognition region coarse positioning：Sobel edge detections are carried out to the bianry image, obtain including logo
Region g ' (x, y)；In region g ' (x, y), the x for meeting formula (1) is chosenmin,xmax,ymin,ymax：
α is taken, the value of β is 1, and passes through formula (2) coarse positioning vehicle-logo recognition region (x, y, α W, β H)：
W is the width in vehicle-logo recognition region, and H is the height in vehicle-logo recognition region, and α, β are respectively width, height parameter,
(x, y) is the coordinate of each pixel in vehicle-logo recognition region.
S130, vehicle-logo recognition：
It, can be secondary by adjusting width and height parameter α, β according to specific detection depth requirements after the completion of coarse positioning
Vehicle-logo recognition region is positioned, realizes the identification output of the logo of different depth.It is specific as follows：
When identifying logo major class, in α=0.2, the vehicle-logo recognition region of β=0.2 carries out the extraction of HOG and PCA features,
Logo major class is identified by logo grader, such as：Masses, Audi, BMW, Buick etc..
When identifying logo major class and subclass, HOG and PCA spies are carried out in the vehicle-logo recognition region of α=1.3, β=0.2
The extraction of sign identifies logo major class and subclass, such as by logo grader：Masses-Santana, masses-Jetta, BMW-Q5,
Buick-is triumphant more etc..
Identification logo major class, subclass and prescribe a time limit in year, in α=1.3, the vehicle-logo recognition region of β=0.6 carry out HOG and
The extraction of PCA features identifies logo major class, subclass and the time limit, such as by logo grader：Masses-Santana -2006-
2007 sections, Audi's-AL4-2004 moneys etc..
Above-mentioned logo grader is trained by following steps and is obtained：
Logo samples pictures are pre-processed by picture pre-treatment step S110.
Vehicle-logo recognition region is positioned in logo samples pictures by vehicle-logo recognition region coarse positioning step S120.
The extraction of HOG and PCA features is carried out in vehicle-logo recognition region.
Logo grader is trained with HOG and PCA features.
It is found in (the including preceding board and tail board) collection of a large amount of vehicle pictures and analytic process, the vehicle pictures of preceding board
Logo LOGO is relatively apparent, and is respectively positioned on the vertical centre position of detection vehicle, therefore can be directly by being based on logo LOGO extractions
Textural characteristics realize the detection and identification of logo, and the positions logo LOGO of tail board are not fixed, and include the vertical centre of vehicle
Position (above car plate, below car plate), the horizontal position (left or right side) of vehicle, thus it is traditional based on positioning logo LOGO extractions
The method that textural characteristics realize vehicle-logo recognition realizes that difficulty is larger in being identified to the detection of tail board logo.And it is of the invention,
In the case of navigating to logo test position information for the first time, use can be effectively expanded by adjusting width and height parameter α, β
In the logo detection zone of feature extraction, the same of the feature weight of logo differentiation in reduction unification is carried out based on logo LOGO
When, it adds based on multiple characteristic features weights for characterizing logo attribute such as car light, wagon flow line style so that the identification of logo
It is characterized in combination with to multiple characteristic synthetics, the final differentiation realized to logo, therefore the method for discrimination of the logo proposed in the present invention,
Effectively logo feature type is expanded so that the identification of logo no longer solely relies on logo LOGO, efficiently solves and is based on
Logo LOGO carries out logo and differentiates there is vehicle-logo recognition problem under the conditions of blocking and damage in logo, while also efficiently solving
Tail board logo position is not fixed caused vehicle-logo recognition problem.Board vehicle-logo recognition before the present invention can not only realize, but also can realize tail
Board vehicle-logo recognition, and it can be directed to actual demand, select major class, major class+subclass style or major class+subclass style+time limit
Logo exports.
As shown in Fig. 2, a kind of vehicle-logo recognition system, including picture pretreatment unit 110, vehicle-logo recognition region coarse positioning list
Member 120 and vehicle-logo recognition unit 130.
Picture pretreatment unit 110 is normally at horizontal top and the logo of car plate for using for reference priori, logo
It is normally in the vertical axis of symmetry of the front/rear face of vehicle, binary conversion treatment is carried out to the video pictures comprising vehicle, obtains two-value
Image g (x, y)；
Vehicle-logo recognition region coarse positioning unit 120 is used to carry out sobel edge detections to the bianry image, including
Vehicle target area g ' (x, y)；In region g ' (x, y), the x for meeting formula (1) is chosenmin,xmax,ymin,ymax：
α is taken, the value of β is 1, and passes through formula (2) coarse positioning vehicle-logo recognition region (x, y, α W, β H)：
W is the width in vehicle-logo recognition region, and H is the height in vehicle-logo recognition region, and α, β are respectively width, height parameter,
(x, y) is the coordinate of each pixel in vehicle-logo recognition region.
Vehicle-logo recognition unit 130 is used for：
When identifying logo major class, in α=0.2, the vehicle-logo recognition region of β=0.2 carries out the extraction of HOG and PCA features,
Logo major class is identified by logo grader, such as：Masses, Audi, BMW, Buick etc..
When identifying logo major class and subclass, HOG and PCA spies are carried out in the vehicle-logo recognition region of α=1.3, β=0.2
The extraction of sign identifies logo major class and subclass, such as by logo grader：Masses-Santana, masses-Jetta, BMW-Q5,
Buick-is triumphant more etc..
Identification logo major class, subclass and prescribe a time limit in year, in α=1.3, the vehicle-logo recognition region of β=0.6 carry out HOG and
The extraction of PCA features identifies logo major class, subclass and the time limit, such as by logo grader：Masses-Santana -2006-
2007 sections, Audi's-AL4-2004 moneys etc..
Above-mentioned logo grader is trained by following steps and is obtained：
Logo samples pictures are pre-processed by picture pre-treatment step S110.
Vehicle-logo recognition region is positioned in logo samples pictures by vehicle-logo recognition region coarse positioning step S120.
The extraction of HOG and PCA features is carried out in vehicle-logo recognition region.
Logo grader is trained with HOG and PCA features.
But those of ordinary skill in the art it should be appreciated that more than embodiment be intended merely to illustrate this
Invention, and be not used as limitation of the invention, as long as in the spirit of the present invention, to embodiment described above
Variation, modification will all fall within the scope of claims of the present invention.
Claims (4)
1. a kind of automobile logo identification method, includes the following steps：
Picture pre-processes：Binary conversion treatment is carried out to the video pictures comprising vehicle, obtains bianry image；
Vehicle-logo recognition region coarse positioning：Sobel edge detections are carried out to the bianry image, obtain including vehicle target area g '
(x,y)；In the region g ' (x, y), the x for meeting formula (1) is chosenmin,xmax,ymin,ymax：
,
And vehicle-logo recognition region (x, y, W, H) is positioned by formula (2)：
It is characterized in that, setting α, β is length and width parameter；Taking α, the value of β is 1, be by the formula (2) coarse positioning identification region (x,
y,αW,βH)；
Vehicle-logo recognition：When identifying logo major class, HOG and PCA features are carried out in the vehicle-logo recognition region of α=0.2, β=0.2
Extraction identifies logo major class by logo grader；When identifying logo major class and subclass, in α=1.3, the logo of β=0.2
Identification region carries out the extraction of HOG and PCA features, and logo major class and subclass are identified by logo grader；Identify logo
Major class, subclass and year prescribe a time limit, and in α=1.3, the vehicle-logo recognition region of β=0.6 carries out the extraction of HOG and PCA features, leads to
Cross logo grader identification logo major class, subclass and the time limit.
2. a kind of automobile logo identification method according to claim 1, which is characterized in that the logo grader passes through following step
Rapid training obtains：
Logo samples pictures are pre-processed by the picture pre-treatment step；
Vehicle-logo recognition region is positioned in the logo samples pictures by the vehicle-logo recognition region coarse positioning step；
The extraction of HOG and PCA features is carried out in the vehicle-logo recognition region；
The logo grader is trained with HOG the and PCA features.
3. a kind of vehicle-logo recognition system, including：
Picture pretreatment unit obtains bianry image for carrying out binary conversion treatment to the video pictures comprising vehicle；
Vehicle-logo recognition region coarse positioning unit is obtained for carrying out sobel edge detections to the bianry image comprising logo
Region g ' (x, y)；In the region g ' (x, y), the x for meeting formula (1) is chosenmin,xmax,ymin,ymax：
It is characterized in that, taking α, the value of β is 1, and passes through formula (2) coarse positioning vehicle-logo recognition region (x, y, α W, β H)：
α, β are length and width parameter；
Vehicle-logo recognition unit, for identification when logo major class, in α=0.2, the vehicle-logo recognition region of β=0.2 carry out HOG and
The extraction of PCA features identifies logo major class by logo grader；Identify logo major class and when subclass, in α=1.3, β=
0.2 vehicle-logo recognition region carries out the extraction of HOG and PCA features, and logo major class and subclass are identified by logo grader；
It identifies logo major class, subclass and prescribes a time limit in year, HOG and PCA features are carried out in the vehicle-logo recognition region of α=1.3, β=0.6
Extraction identifies logo major class, subclass and the time limit by logo grader.
4. a kind of vehicle-logo recognition system according to claim 3, which is characterized in that the logo grader passes through following step
Rapid training obtains：
Logo samples pictures are pre-processed by the picture pre-treatment step；
Vehicle-logo recognition region is positioned in the logo samples pictures by the vehicle-logo recognition region coarse positioning step；
The extraction of HOG and PCA features is carried out in the vehicle-logo recognition region；
The logo grader is trained with HOG the and PCA features.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510021400.5A CN104504384B (en) | 2015-01-15 | 2015-01-15 | Automobile logo identification method and its identifying system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510021400.5A CN104504384B (en) | 2015-01-15 | 2015-01-15 | Automobile logo identification method and its identifying system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104504384A CN104504384A (en) | 2015-04-08 |
| CN104504384B true CN104504384B (en) | 2018-09-21 |
Family
ID=52945780
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510021400.5A Active CN104504384B (en) | 2015-01-15 | 2015-01-15 | Automobile logo identification method and its identifying system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104504384B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105160340A (en) * | 2015-08-31 | 2015-12-16 | 桂林电子科技大学 | Vehicle brand identification system and method |
| CN105574490B (en) * | 2015-12-10 | 2019-04-09 | 金鹏电子信息机器有限公司 | Vehicle brand recognition methods and system based on headlight characteristics of image |
| CN105844286A (en) * | 2016-03-11 | 2016-08-10 | 博康智能信息技术有限公司 | Newly added vehicle logo identification method and apparatus |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101196980A (en) * | 2006-12-25 | 2008-06-11 | 四川川大智胜软件股份有限公司 | Method for accurately recognizing high speed mobile vehicle mark based on video |
| CN102419820A (en) * | 2011-08-18 | 2012-04-18 | 电子科技大学 | Method for rapidly detecting car logo in videos and images |
| CN103093230A (en) * | 2013-01-21 | 2013-05-08 | 信帧电子技术（北京）有限公司 | Detecting method and device of vehicle logo |
| US8934676B2 (en) * | 2012-05-04 | 2015-01-13 | Xerox Corporation | Robust character segmentation for license plate images |
-
2015
- 2015-01-15 CN CN201510021400.5A patent/CN104504384B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101196980A (en) * | 2006-12-25 | 2008-06-11 | 四川川大智胜软件股份有限公司 | Method for accurately recognizing high speed mobile vehicle mark based on video |
| CN102419820A (en) * | 2011-08-18 | 2012-04-18 | 电子科技大学 | Method for rapidly detecting car logo in videos and images |
| US8934676B2 (en) * | 2012-05-04 | 2015-01-13 | Xerox Corporation | Robust character segmentation for license plate images |
| CN103093230A (en) * | 2013-01-21 | 2013-05-08 | 信帧电子技术（北京）有限公司 | Detecting method and device of vehicle logo |
Non-Patent Citations (1)
| Title |
|---|
| 智能交通系统中车标图像识别技术研究;魏平顺;《中国优秀硕士学位论文全文数据库 信息科技辑》;20130715(第7期);摘要，正文第20-48页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104504384A (en) | 2015-04-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102043950B (en) | Vehicle outline recognition method based on canny operator and marginal point statistic | |
| US9092696B2 (en) | Image sign classifier | |
| CN103235938B (en) | The method and system of car plate detection and indentification | |
| CN103136528B (en) | A kind of licence plate recognition method based on dual edge detection | |
| CN103198315B (en) | Based on the Character Segmentation of License Plate of character outline and template matches | |
| CN105335743A (en) | Vehicle license plate recognition method | |
| CN106650553A (en) | License plate recognition method and system | |
| CN103824091B (en) | A kind of licence plate recognition method for intelligent transportation system | |
| CN107657225B (en) | Pedestrian detection method based on aggregated channel characteristics | |
| CN202084185U (en) | Automatic traffic sign identification device | |
| CN102968646A (en) | Plate number detecting method based on machine learning | |
| CN104751142A (en) | Natural scene text detection algorithm based on stroke features | |
| CN105447503A (en) | Sparse-representation-LBP-and-HOG-integration-based pedestrian detection method | |
| CN106096667A (en) | Bill images sorting technique based on SVM | |
| CN103390167A (en) | Multi-characteristic layered traffic sign identification method | |
| CN103824081A (en) | Method for detecting rapid robustness traffic signs on outdoor bad illumination condition | |
| CN106355180B (en) | A kind of license plate locating method combined based on color with edge feature | |
| CN105551261A (en) | False-license-plate vehicle detection method and system | |
| CN104268596A (en) | License plate recognizer and license plate detection method and system thereof | |
| CN103927548B (en) | Novel vehicle collision avoiding brake behavior detection method | |
| CN102902957A (en) | Video-stream-based automatic license plate recognition method | |
| CN102542244A (en) | Face detection method and system and computer program product | |
| CN103679146A (en) | Safety belt intelligent detection method based on high-pass filter and Hough conversion | |
| CN102880863A (en) | Method for positioning license number and face of driver on basis of deformable part model | |
| CN103902985A (en) | High-robustness real-time lane detection algorithm based on ROI |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 201209 Chuansha Road, Shanghai, No. 221, room 11, building 955 Applicant after: Bocom Intelligent Network Technology Co. Ltd. Address before: 201209 Chuansha Road, Shanghai, No. 221, room 11, building 955 Applicant before: BOCOM Smart Network Technologies Inc. |
|
| COR | Change of bibliographic data | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 201201 221, room 11, 955 Chuansha Road, Pudong New Area, Shanghai. Applicant after: New wisdom cognition Marketing Data Services Ltd Address before: 201209 221, room 11, 955 Chuansha Road, Pudong New Area, Shanghai. Applicant before: Bocom Intelligent Network Technology Co. Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |