SG11201809816YA - Vehicle identification method and apparatus - Google Patents

Vehicle identification method and apparatus

Info

Publication number
SG11201809816YA
SG11201809816YA SG11201809816YA SG11201809816YA SG11201809816YA SG 11201809816Y A SG11201809816Y A SG 11201809816YA SG 11201809816Y A SG11201809816Y A SG 11201809816YA SG 11201809816Y A SG11201809816Y A SG 11201809816YA SG 11201809816Y A SG11201809816Y A SG 11201809816YA
Authority
SG
Singapore
Prior art keywords
vehicle
feature
image
color
color feature
Prior art date
Application number
SG11201809816YA
Inventor
Jianzong Wang
Jing Xiao
Original Assignee
Ping An Technology Shenzhen Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Ping An Technology Shenzhen Co Ltd filed Critical Ping An Technology Shenzhen Co Ltd
Publication of SG11201809816YA publication Critical patent/SG11201809816YA/en

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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
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G06F18/24155—Bayesian classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/29—Graphical models, e.g. Bayesian networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • 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
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • 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
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • 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
    • G06V2201/08—Detecting or categorising vehicles

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Multimedia (AREA)
  • Business, Economics & Management (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Mathematical Physics (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Technology Law (AREA)
  • General Business, Economics & Management (AREA)
  • Probability & Statistics with Applications (AREA)
  • Image Analysis (AREA)

Abstract

The present application is applicable to the technical field of image recognition, and provides a method and device for vehicle identification, the method comprises: obtaining a first vehicle image and a second vehicle image, wherein the first vehicle image comprises a first vehicle, and the second vehicle image comprises a second vehicle; extracting a first color feature and a first vehicle type feature of the first vehicle, and extracting a second color feature and a second vehicle type feature of the second vehicle based on a convolutional neural network model; combining the first color feature and the first vehicle type feature into a first vehicle feature, and combining the second color feature and the second vehicle type feature into a second vehicle feature; calculating a similarity parameter between the first vehicle feature and the second vehicle feature; and determining whether the first vehicle is the same one as the second vehicle according to the similarity parameter. In the present application, by taking the advantage of the convolution neural network model in the aspect of image feature extraction, by using the vehicle color feature and the vehicle type feature to perform a vehicle verification, and by digging out differences among the features through training the joint Bayesian model, so that whether the same vehicle is recorded in two images can be accurately recognized.
SG11201809816YA 2017-02-16 2018-01-31 Vehicle identification method and apparatus SG11201809816YA (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710083593.6A CN107688819A (en) 2017-02-16 2017-02-16 The recognition methods of vehicle and device
PCT/CN2018/074878 WO2018149302A1 (en) 2017-02-16 2018-01-31 Vehicle identification method and apparatus

Publications (1)

Publication Number Publication Date
SG11201809816YA true SG11201809816YA (en) 2018-12-28

Family

ID=61152346

Family Applications (1)

Application Number Title Priority Date Filing Date
SG11201809816YA SG11201809816YA (en) 2017-02-16 2018-01-31 Vehicle identification method and apparatus

Country Status (4)

Country Link
US (1) US10740927B2 (en)
CN (1) CN107688819A (en)
SG (1) SG11201809816YA (en)
WO (1) WO2018149302A1 (en)

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US10417527B2 (en) * 2017-09-06 2019-09-17 Irdeto B.V. Identifying an object within content
CN110795974B (en) * 2018-08-03 2023-04-07 中国移动通信有限公司研究院 Image processing method, device, medium and equipment
US11037440B2 (en) * 2018-12-19 2021-06-15 Sony Group Corporation Vehicle identification for smart patrolling
CN110059101B (en) * 2019-04-16 2021-08-13 北京科基中意软件开发有限公司 Vehicle data searching system and method based on image recognition
CN110135517B (en) * 2019-05-24 2023-04-07 北京百度网讯科技有限公司 Method and device for obtaining vehicle similarity
CN110363193B (en) * 2019-06-12 2022-02-25 北京百度网讯科技有限公司 Vehicle weight recognition method, device, equipment and computer storage medium
CN111062396B (en) * 2019-11-29 2022-03-25 深圳云天励飞技术有限公司 License plate number recognition method and device, electronic equipment and storage medium
CN111027499A (en) * 2019-12-17 2020-04-17 北京慧智数据科技有限公司 Convolutional neural network-based low-emission restriction identification method for trucks
CN111709332B (en) 2020-06-04 2022-04-26 浙江大学 Dense convolutional neural network-based bridge vehicle load space-time distribution identification method
CN111709352B (en) * 2020-06-12 2022-10-04 浪潮集团有限公司 Vehicle scratch detection method based on neural network
CN113506400A (en) * 2021-07-05 2021-10-15 深圳市点购电子商务控股股份有限公司 Automatic vending method, automatic vending device, computer equipment and storage medium
US20240104865A1 (en) * 2022-09-22 2024-03-28 Faro Technologies, Inc. Feature extraction using a point of a collection of points

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US9665802B2 (en) * 2014-11-13 2017-05-30 Nec Corporation Object-centric fine-grained image classification
US9418319B2 (en) * 2014-11-21 2016-08-16 Adobe Systems Incorporated Object detection using cascaded convolutional neural networks
CN104361359A (en) * 2014-11-25 2015-02-18 深圳市哈工大交通电子技术有限公司 Vehicle recognition method based on image detection
US9633282B2 (en) * 2015-07-30 2017-04-25 Xerox Corporation Cross-trained convolutional neural networks using multimodal images
CN105184271A (en) 2015-09-18 2015-12-23 苏州派瑞雷尔智能科技有限公司 Automatic vehicle detection method based on deep learning
CN105354273A (en) * 2015-10-29 2016-02-24 浙江高速信息工程技术有限公司 Method for fast retrieving high-similarity image of highway fee evasion vehicle
CA3008323A1 (en) * 2015-12-15 2017-06-22 Applied Recognition Inc. Systems and methods for authentication using digital signature with biometrics
CN105654066A (en) 2016-02-02 2016-06-08 北京格灵深瞳信息技术有限公司 Vehicle identification method and device
WO2017143260A1 (en) * 2016-02-19 2017-08-24 Reach Consulting Group, Llc Community security system
CN105631440B (en) * 2016-02-22 2019-01-22 清华大学 A kind of associated detecting method of vulnerable road user
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Also Published As

Publication number Publication date
US10740927B2 (en) 2020-08-11
US20190139262A1 (en) 2019-05-09
WO2018149302A1 (en) 2018-08-23
CN107688819A (en) 2018-02-13

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