The Vehicle License Plate Location Based on Adaboost algorithm. of Color Feature

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1 Internatonal Journal of Research n Engneerng and Scence (IJRES) ISSN (Onlne): ISSN (Prnt): Volue 4 Issue 8 ǁ August ǁ PP The Vehcle Lcense Plate Locaton Based on Adaboost algorth of Color Feature Lu Xu Wu Xuncheng Zhang Wewe (College Of Autooble Engneerng Shangha Unversty Of Engneerng Scence Shangha chna) Abstract: Suarzed Thethe Related Research Of The Lcense Plate Locaton At Hoe And Abroad In Recent Years Ths Artcle Proposed A Lcense Plate Locaton Method Whch Based On Adaboost Algorth. At Frst The Noral Iages Were Converted Into HSV Iage.Accordng To The Characterstcs Of The Color In HSV Iage The Lcense Plate Iage Bnarzaton Processng Was Copleted;Thedenosng Processng Was Copleted By Morphology;Then Based On Adaboost Algorth Off-Lne Traned Classfer Was Used To Detect Lcense Plate Canddate Area Fnally The Lcense Plate Localzaton Was Postoned. Keywords: Lcense Plate Locaton; Adaboost; Opencv I. INTRODUCTION Lcense Plate Recognton Is A Knd Of Autoatc Identfcaton Technology By The Use Of Dynac Vdeo Or Stll Iages Of The Vehcle Lcense Plate Nuber Pcture Color.A Coplete Lcense Plate Recognton Syste Generally Includes Fve Man Parts: Acqurng Iages Preprocessng The Iage Preprocessng Iage Deltng Character And Recognzng Character.Aong The The Lcense Plate Locaton Technology Is The Bass And Prese Of Lcense Plate Recognton Syste Is Also The Key To Iprove The Whole Lcense Plate Recognton Syste Recognton Rate. Lcense Plate Recognton Is Manly Coposed Of Four Modules: The Iage Acquston Module The Lcense Plate Locatng Module The Character Segentaton Module And The Character Recognton Module [1]. The Overall Archtecture Of The Lcense Plate Recognton Syste Is Shown In Fgure 1-1. Fgure1-1 Overall Archtecture Of The Lcense Plate Recognton Syste II. III. LICENSE PLATE LOCATION BASED ON ADABOOST ALGORITHMOF COLOR FEATURES Ths Artcle Is Based On An Iproved Adaboost Algorth To Acheve A Knd Of Lcense Plate Locaton Syste.Ths Whole Syste Conssts Of Several StepsAs Is Shown In Fgure 2-1.FrstThe Iages Was CollectedThenoral Iages Were Converted Into HSV Iage.Accordng To The Characterstcs Of The Color In HSV Iage The Lcense Plate Iage Bnarzaton Processng Was Copleted;Thedenosng Processng Was Copleted By Morphology;Then Based On Adaboost Algorth Off-Lne Traned Classfer Was Used To Detect Lcense Plate Canddate Area Fnally The Lcense Plate Localzaton Was Postoned [1]. 40 Page

2 The Vehcle Lcense Plate Locaton Based On Adaboostalgorth Of Color Feature Fgure 2-1Lcense Plate Locatng Method Process 2.1 Lcense Plate Color Analyss Accordng To Lcense Plate Color Classfcaton And The Nature Of The HSV Color Model The Color Hstogra Was Used To Statstcal The Blue Whte Color Coponent Szes In The HSV Color Model Accounts For The Proporton Of The Total Pxels Iage And The Lcense Proporton Dstrbuton Of Each Color Coponent Was Obtaned Whch As The Bass Of Color Feature Extracton [1]. HSV Iage Of Each Color Group Is Shown In Fgure 2-2: Fgure 2-2 HSV Color Space In Varous Color Group Based On Color Feature Statstcs The Scope Of The Color Segentaton Threshold Was Deterned.The Blue &Whte Plates Was Relatedwth H& Stwo Coponent Color Segentaton Need To Identfy The H S Two Weght Threshold Range The Results Is Shown In Table 3-1. Table 3-1 Plate HSV Coponent Table H Coponent S Coponent Lcense Plate 60<H< <S< Page

3 The Vehcle Lcense Plate Locaton Based On Adaboostalgorth Of Color Feature 2.2 Lcense Plate Color Pretreatent Bnarzaton Once The Scope Of Lcense Plate Color Segentaton Threshold Was Deterned The Unrelated Colorcan Be Fltered Out Accordng To The Scope Of The Threshold Value [2].Bnarzaton Is One Of All The Pxels On The Iage Grey Value Wth Two Values Whch Represented By 0 And 1 Naely Can Make The Iage Clear Black And Whte Effect.Assung That A Contnuous Gray Iage Is f ( x y ) g The Bnary Iage Is ( x y ) As Is Shown In Forula (2-1). 1 f ( ) ( x y ) T g x y (2-1) Properly Selected Of T Can Correctly To Separate 0 f ( The x ybackground ) T Fro The Object To Reduce The Iage Inforaton Easy To Iage The Extracton Of Useful Inforaton.Bnarzaton Process Wth The Threshold Value Whcheetthe 60<H<170 And 120<S<255.The Result Is Shown In Fgure 2-3: Fgure2-3Bnarzaton HSV Iage Matheatcal Morphology Processng In The Extracton Of Color Iage The Change Of The Background Envronent Wll Cause Soe Slar To Lcense Plate Hue Saturaton And Brghtness Of Pont Or AreaWhch Can Lead To A Bnary Iage Into Soe Knd Of Nose Or Interference.Here The Method Of Matheatcal Morphology Processngcan Be Very Good To Solve The Proble Of Bnary Iage Nose Reoval Is Conducve To The Establshent Of The Interest Area And Poston The Target After Calbraton [2] Eroson Erosons A Progress Of Elnatng The Boundary Pont In Morphology Whch Make The Boundary Inward Contracton And Can Be Used To Elnate The Nose In Iage Bnarzaton [3].For The A And B In Collecton Z And B Was Used Toeroson A Whch Expressed Wth A B And Shownn Forula (2-2). AB ={z (B) z A} (2-2) As Shown In Fgure 2-4 For The Color Iage Of The Two Value Of The Corroson Process. Fgure2-4Eroson Treatent Of Bnary Iage 42 Page

4 The Vehcle Lcense Plate Locaton Based On Adaboostalgorth Of Color Feature Dlaton In Morphology Dlatonand Corroson Are On The Contrary Whch Can Expand Bnary Iage Pxel Boundary Pont.Makng All The Background Pont Merge Into The Object Whch Is The Process Of The Boundary To External Expanson [4]. A And B Were The Meber Of The Z Collecton A Was Dlated By B Whch Expressed As A B And Defned As ^ AB={z ( B) A } (2-3) z When Selectng The Hgh Resoluton Iage AcqustonThe Lcense Plate Iage In The Iage Is LargerWhch Can Operate Nose Processngthrough The Secondary Corroson And Expanson As Is Shown In The Forula (2-4) And Wll Not Lead To Corroson Of The Target Are Treated When In Nose Reoval [5] ; Whle The Saller Iage Resoluton Has To Adopt A Corroson And Expanson Operaton Naely The Forula (2-5). P (( CT ) T) (2-4) P (((( CT ) T ) T) T) (2-5) Accordng To The Above Color Segentaton And Morphologcal Operaton Can Coplete The Extracton Of Lcense Plate Canddate Area Can Be Copleted As Is Shown In Fgure 2-5. Fgure2-5Dlatontreatent Of Bnary Iage IV. LICENSE PLATE LOCALIZATION BASED ON ADABOOSTALGORITHM 3.1 Tradtonal Adaboostalgorth Prncple In The Fraework Of Probablstc Approxaton (PAC) A Concept If There Is A Learnng Algorth Of Polynoal Can Be Learned And The Accuracy Is Very Hgh Then Called The Concept Can Be Learned Strongly; A Concept If There Is A Learnng Algorth Of Polynoal Can Be Learned And The Accuracy Is Better Than Rando Guesses Slghtly Then Called Ths Concept Is Too Weak To Learn. For Adaboost Algorth By Rasng Those In The Prevous Round Of Saple Weght Of The Classfer Error Classfcaton And Reducng The Saple Weght Of The Correct Classfcaton To Change The Rght Value And The Probablty Dstrbuton Of Tranng Data. 3.2 Iprovedadaboost Algorth Whle Tradtonal Adaboost Algorth By Weght Update Rules Can Be Better Traned ClassferHowever There Is A Degradaton. Dffcult Saples Has Hgh WeghtIn Each Classfcaton Iteratve Algorth Dffcult Saple Is Assgned Hgh WeghtThen It Causedegradaton [1]. A Drect Result Of The Consequences Of Degradaton Are Those Generated Relatvely Good Classfcaton In The Subsequent Learnng Process As The Weght Dstrbuton Wll Be Offset Gradually Destroyed Or Dscarded And Ultately The 43 Page

5 The Vehcle Lcense Plate Locaton Based On Adaboostalgorth Of Color Feature Whole Syste Perforance. In Ths Paper The Tradtonal Adaboost Algorth Was IprovedThe Partcular Way Is In The Process Of Tranng In Each Iteraton Defne A New Threshold Accordng To The Saples Are Whether Classfed HW Correctly And Whether The Current Weght Greater Than These Two Aspects Update The Weghts.Redesgned Classfer Is As Follows: The Known Concentraton Have N (x Tranng Saples 1y 1)(x 2y 2)...(x Ny ) N Aong The There Are a Negatve Saples And b 1w Postve Saples Supposng That Indcates The 1 w Thsaple Error Weghts Of The y 0 Thcycle.To Intalze The Weghts: w 2a y 1 ;Whle 2N.The Specfc Tranng Process Is As Follows: (1) Noralze The Weght w f w (3-1) n j N h (2)For Feature The Tranng Weak Classfer w The Deterned Threshold j p And Inequalty Sgn j Drecton Make The Functon j w j G ( ) 1 x y j1 (3-2) Mnze As Much As Possble; h (3) Choosng A Mnu Error Rate Classfer t N ; (4) Update The Threshold w j1 HW (3-3) (5) Update The Saple Weght N w Z G ( x ) y 1 w 1 ( ) & Z w G x y w HW w G ( x ) y & w HW Z (3-4) In The Forula 1 Weght. Fnally The Strong Classfer Is: Gx ( ) Z Is The Noralzaton Factor For 1 G ( x) other (3-5) N j1 w 1 HW Is Updated In The Forula 1 ln Known Fro The Analyss Of The Above Only When The Saple Is Wrong Classfed And Cobned Wth The Current Weght Is Less Than The Update Threshold At Ths Te Of The Saple Weght Wll Increase. On The Other Hand Its Weght Wll Be Reduced Thus To Soe Extent Allevate The Degradaton Phenoenon In Lcense Plate Locatng Daage. 3.3 Lcense Plate Localzaton Based Onadaboost Algorth When Detect The Target Based On Statstcal Learnng Method Frst Of All A Large Nuber Of Saples Are Needed And Then Make Postve And Negatve Saples Lbrary Use Machne Learnng Algorth To Tran A Classfer As A Detecton Model Of The Target And At Last The Classfer Is Used In The Iage Search So As To Detect The Target [6]. 44 Page

6 The Vehcle Lcense Plate Locaton Based On Adaboostalgorth Of Color Feature Postve Saples Manly Coes Fro The Internet Search Ths Experent USES The Dfferent Provnces Of Lcense Plate Iages Under Dfferent Lght Condtons And Unfed Set To 150 * 50 Pxels. The Tranng Saples Are Shown In Fgure 3-1 : Fgure3-1Postve Saple Of Lcense Plate Negatve Saples Were Randoly Selected Fro The Iages Whch Do Not Contan The Target Iage. Negatve Saples Is Shown In Fgure 3-2. Fgure3-2Negatve Saple Of Lcense Plate 3.4 Lcense Plate Locatng Experent Based On Adaboostalgorth Based On The Above Algorth Ths Paper Use QT Opencv3.0 Ipleents Lcense Plate Locatng Experent [1].Through The Tranng Of The Postve And Negatve Saples Eventually Get The Lcense Plate Classfer Through Color Iage Bnarzaton And Morphology Denosng And Then Search In The 45 Page

7 The Vehcle Lcense Plate Locaton Based On Adaboostalgorth Of Color Feature Canddate Area Fnally Get The Target Detecton Result. Accordng To The Method Descrbed In Ths Artcle A Large Nuber Of Caera Car Iages Was Tested The Experental Results Were Satsfactory [7]. The Operatng Syste Is Mcrosoft Wndows 7The Developent Tools Is QT5.7.0+Opencv3.0The Applcaton Software Is The Lcense Plate Localzaton Based On Adaboost Algorth Progra The Progra Interface Is Shown In Fgure 3-3. Fgure3-3Progra Interface As Seen Fro The Table 3-1 The Recognton Rate The Use Of The Iproved Adaboost Algorth For Lcense Plate Locaton Copared Wth The Tradtonal Adaboost Algorth Is Hgher. Table 3-1Experental Coparson Of Two Dfferent Algorths The Brghtness Sunny Day Cloudy Day Nght Nuber Of Iages Tradtonal Adaboost Algorth Iproved Adaboost Algorth The Nuber Of Accurate Postonng Accurate Probablty 90.8% 87.5% 88.0% The Nuber Of Accurate Postonng Accurate Probablty 92.6% 90.8% 90.0% V. CONCLUSION Based On The Suary Lcense Plate Postonng Of The Related Research At Hoe And Abroad In Recent Years Was Proposed Based On Lcense Plate Color Features Based On Adaboost Algorth Of Lcense Plate Locatng Method. Change Method In Lcense Plate Locatng The Frst Iages Into HSV Iage Accordng To The Characterstcs Of The Color In HSV Iage Of Lcense Plate Iage Bnarzaton Processng Morphologcal Processng And Denosng. Then Use Offlne Tranng Classfer Based On Iproved Adaboost Algorth To Detect Lcense Plate Canddate Area And On QT Opencv3.0 Platfor To Wrte The Progra Lcense Plate Recognton Fnally Coplete The Lcense Plate Locatng. And Copared Wth Tradtonal Adaboost Algorth Through Adaboost Algorth Based On Iproved Classfer Can Be More Accurately Identfy The Lcense Plate Area Under Dfferent Illunaton Condtons. [1]. REFERENCES [2]. Huangzefeng.The Research And Ipleentaton Of Vehcle Lcense Platelocaton Technology Based On Adaboost [J] : [3]. Luoyuca. Research On Vehcle Lcense Plate Locatonalgorth Based On Partcle Flterbased On Iproved Adaboostalgorth[J] :45. [4]. LI SI. Face Detecton Technology Based On Adaboost Algorth [J] : Page

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