Comparison of SVMs in Number Plate Recognition

Transcription

1 Comparso of SVMs Number Plate Recogto Lhog Zheg, Xaga He ad om Htz Uversty of echology, Sydey, Departmet of Computer Systems, {lzheg, sea, Abstract. Hgh accuracy ad hgh speed are two key ssues to cosder automatc umber plate recogto (ANPR). I ths paper, we costruct a recogto method based o Support Vector Maches (SVMs) for ANPR. Frstly, we brefly revew some kowledge of SVMs. he, the umber plate recogto algorthm s proposed. he algorthm starts from a collecto of samples of characters. he characters the umber plates are dvded to two kds, amely dgts ad letters. Each character s recogzed by a SVM, whch s traed by some kow samples advace. I order to mprove recogto accuracy, two approaches of SVMs are appled ad compared. Expermetal results based o two algorthms of SVMs are gve. From the expermetal results, we ca make the cocluso that oe agast oe method based o RBF kerel s better tha others such as ductve learg-based or oe agast all method for automatc umber plate recogto. Itroducto Number recogto s playg a mportat role mage processg feld. For example, there are thousads of cotaers ad trucks eed to be regstered every day at cotaer termals ad depots. Normally, ths regstrato wll be doe maually. However, ths s ot oly proe to error but also slow to meet the creasg volume of cotaers ad trucks. Hece, a automatc, fast ad precse umber recogto process s requred. he fudametal ssues umber plate recogto are the requremets of hgh accuracy ad hgh recogto speed. Sce last two decades, varous commercal ANPR products (Zheg, He ad L 2005) aroud the world are avalable, such as SeeCar Israel, VECON Hogkog, LPR USA, the ANPR UK, IMPS Sgapore, ad the CARINA Hugary (Zheg 2005). Eve though there have bee so may successful ANPR systems, there are stll several problems for character recogto of umber plates. he followg three problems are the most crtcal. Frstly, the recogto system must be able to hadle varous szes, fots, spaces ad algmets of the characters the umber plates. Secodly, the recogto system

2 2 Lhog Zheg, Xaga He ad om Htz must be robust to chages llumato ad colors used. hrdly, the recogto system must be able to dstgush the obscured characters real-lfe mages due to rust, mud, peelg pat, ad fadg color. o resolve the problems above, a effectve method must have a geeral adaptablty to dfferet codtos. It should have good tolerace for ose ad classfy ad recogze the characters umber plate accurately ad credbly. I order to mprove the performace of recogto, a algorthm o umber recogto was proposed (Aksoy, Cagl ad urker 2000) based o RULES-3 ducto theory. hs algorthm tras character samples ad obtas the rules that are used to recogze the umbers o umber plates. Oe advatage of usg ths method s that the recogto speed s much qucker umber recogto. But t s ot robust to mage rotato, traslato ad scalg. However, t caot dstgush dgts 6 ad 9 wthout addtoal observato. I order to mprove the recogto performace, we propose aother algorthm to umber recogto (Zheg ad He 2006). hs techque uses a Support Vector Mache (SVM) to tra character samples ad obta the rules that are used to recogze the umbers o umber plates. SVM (Crsta 2000; Vapk 999) s forcefully competg wth may methods for patter classfcato. A SVM s a supervsed learg techque frst dscussed by Vapk (Vapk 999). SVM takes Statstcal Learg heory (SL) as ts theoretcal foudato, ad the structural rsk mmzato as ts optmal obect to realze the best geeralzato. hey are based o some smple deas ad provde a clear tuto of what learg from examples s all about. More mportatly, they possess the feature of hgh performace practcal applcatos. From 960s to preset, SVMs become more ad more mportat the feld of patter recogto. he orgazato of ths paper s as follows. We frst troduce some basc kowledge of SVMs Secto 2. I Secto 3, mult-class classfer model ad oe agast all ad oe agast oe strategy are brefly troduced. he algorthm of umber plate recogto s doe Secto 4. he expermetal results for umber recogto are demostrated Secto 5. We coclude Secto 6. 2 Prcples of SVMs I 2000, SVM was defed by Crsta & aylor (Crsta ad Shawe-aylor 2000) as a system for effcetly trag lear learg maches kerel-duced feature spaces, whle respectg the sghts of geeralzato theory ad explotg optmzato theory. A SVM s a patter recogzer that classfes data wthout makg ay assumptos about the uderlyg process by whch the observatos were grated. he SVMs use hyperplaes to separate the dfferet classes. May hyperplaes are ftted to separate the classes, but there s oly oe optmal separatg hyperplae. he optmal oe s expected to geeralze well comparso to the others. he optmal hyperplae s determed oly by support vectors, whch are deally dstrbuted ear class boudares. he hyperplae s costructed so as to maxmze a measure of the marg betwee classes. A ew data sample s classfed by the SVM accordg to the decso boudary defed by the hyperplae.

3 Comparso of SVMs Number Plate Recogto 3 A SVM correspods to a lear method a very hgh dmesoal feature space. he feature space s olearly related to the put space. Classfcato s acheved by realzg a lear or o-lear separato surface the feature space (Vapk 999). We brefly descrbe geeral kowledge of SVMs as follows (Zheg ad He 2006). Gve a two-class classfcato problem, separatg hyperplaes ca be defed as: H : w~ ~ x + b w ~, b = 0, where w s a ormal vector, the put s deoted by x ad b s a offset. SVM tres to fd the optmal hyperplae va maxmzg the marg betwee the postve put vectors, {x whe y =+, for =,, }, ad egatve put vectors, {x whe y =-, for =,, }. I the lear case, ths s equvalet to maxmze 2/ w ~ (. s orm of w ~ ) that s regarded as a caocal represetato of the separatg hyperplae,.e., s. t. w~ 2 m 2 y ( < w~, ~ x > + b),. () Here w ~ ca be solved as follows by applyg the Lagraga multpler α. where 0, w~ = α y φ( x ) = α ( =, 2,, ), s the Lagraga multpler, ad φ s the kerel fucto. For a ew put, ts classfed label s accordg to the result of: f H ( x) = sg( w ~ ( x) + b) = sg( α y K( x, x ) + b) w ~, b = φ, where K( x, x ) = φ( x) φ( x ). I the case that the set s ot learly separable or does ot satsfy the equalty costrat y ~ ( < w, x > + b), for all, a slack ad oegatve varableξ s added to Eq. as show by

4 4 Lhog Zheg, Xaga He ad om Htz s. t. w~ 2 m + C ξ. (2) = y ( w ~ 2 φ( x ) + b) ξ, ξ 0, =,..., he term ξ s a upper boud o the umber of msclassfcato the trag = set. It dcates the dstace that the trag pot from the optmal hyperplae ad the amout of volato of the costrats. Furthermore, C s the pealty term for msclassfcatos. C cotrols the trade-off betwee maxmzg the marg ad mmzg the trag error, ad betwee a better geeralzato ad a effcet computato. 3 Mult-class Model of SVMs Amog may classfcato methods, SVM has demostrated superor performace. It has bee successfully utlzed hadwrtte umeral recogto. However, SVM was orgally desged for bary classfcato, ad ts exteso to solve multclass problems s ot straghtforward. he popular methods for applyg SVM to mult-class problems decompose a mult-class problem to may bary-class problems ad corporate may bary-class SVMs. wo ma approaches have bee suggested for applyg SVMs for mult-class classfcato (Foody ad Mathur 2004). I each approach, the uderlyg bass has bee to reduce the mult-class problem to a set of bary problems, ad to eable the use of basc SVM. he frst approach, called oe agast all (Foody ad Mathur 2004; Dog, Sue ad Krzyzak 2005), uses a set of bary classfers, each traed to separate oe class from the rest. For a gve put x, there are k decso fuctos. x s classfed to be the oe of k classes that gves the largest decso value. he secod approach s called oe agast oe. I ths approach, a seres of classfers are appled to each par of classes, ad oly the label of the most commoly computed class s kept for each case. he applcato of ths method requres k(k-)/2 classfers or maches be appled to each par of classes, ad a strategy to hadle staces whch a equal umber of votes are derved for more tha oe class for a case. Oce all k(k-)/2 classfers have bee udertake, the max-w strategy s followed. he mult-class model ca be descrbed as follows. Gve trag data Ω={(x, y ), (x 2, y 2 ),, (x, y ) x R, (,2,..., )}, ad y {,2,3,..., k}, = where k s the umber of classes. he classfcato fucto s as:

5 Comparso of SVMs Number Plate Recogto 5 s. t. 2 m w + C ξ w, b, ξ 2 = ( w ) φ( x ) + b ξ, f y ( w ) ξ 0, φ( x ) + b =,..., + ξ, f y =,, φ φ where K x, x ) = ( x) ( x ) ( I APRN, k s 36, whch cludes 0 for dgts ad 26 for letters. he above formula mples the followg 36 decso fuctos for all 36 dgts ad letters: ( w ) ( x) + b,... φ ( w ) φ( x) + b. A x s classfed to be the dgt or letter a f ts decso fucto gves the maxmum value the SVM for a,.e., Class of x max (( w ) ( x) + b ) arg =,..., 36 φ. Fg.. he umber plate samples 4 Number Plate Classfer Desg he car umber plate at the New South Wales state of Australa has up to sx characters as show Fg.. Usually, the umber plate cossts of two ma sectos. he upper secto cotas ma formato of the umber plate, ad the lower part s for the ame of the state. he upper part s more mportat, ad s separated to two groups of characters. he frst group usually cossts of three or four letters of A to Z ad the secod group cossts of three or two dgts of 0 to 9. I order to speed up the process, two sets of SVMs are desged accordg to these two groups of characters. Oe set of SVMs s desged for recogzg dgtal umbers ad the other oe s desged for letters. he detals of our algorthms are descrbed as follows. For comparso, the oe agast all ad oe agast oe methods are both adopted.

6 6 Lhog Zheg, Xaga He ad om Htz I the frst approach usg oe agast all method, for recogzg the dgts a umber plate, te SVMs are desged for the te dgts from 0 to 9. Each SVM has oe dgtal umber sample as oe label ad all or some of the other samples are as aother label. After trag, each SVM gets ts ow values of parameters. he decso value of the testg sample wll be calculated based o the values of parameters obtaed. he fal recogto result wll be acheved accordg to the class that gves the maxmum decso value. he procedure for recogzg the letters a umber plate s the same as that for dgts except that the total umber of SVMs s 26 for 26 letters. I the secod approach usg oe agast oe method, SVM has oe dgtal umber sample as oe label ad ay oe of the other samples s take as aother label. herefore, 45 SVMs are desged for the te dgts from 0 to 9, ad 325 SVMs are for letter A to Z. We summarze the SVM based algorthm for umber recogto ths paper as follows. I order to recogze a umber plate, we go through the followg steps. Step. Pre-process the mage of umber plate. Step 2. Segmet the mage to several parts of whch each cotas oly a sgle character. Step 3. Normalze each letter or dgt o the umber plate. Step 4. Extract the feature vector of each ormalzed caddate Step 5. Recogzes the sgle character (a dgt or a letter) by the set of SVMs traed advace. Step 6. If there are o more uclassfed samples, the SOP. Otherwse, go to Step 5. Step 7. Add these test samples to ther correspodg database for further trag. Step 8. Recogze umber plate by brgg all characters used together. Whe a umber plate rego s located ad extracted, the hstogram proecto methods are appled for character segmetato. he umber plate s segmeted ad the sub-mages cotag dvdual characters (dgts ad letters) formg the umber plate are obtaed. I the pre-processg step, each sub-mage of a character s ormalzed to a certa sze whch s 20 pxels wdth ad 36 pxels legth. he the sub-mage s barzed to rage of [-, +] for ehacg the character from backgroud. he support vectors are calculated drectly from the barzed submages. he hgh dmesoal feature vectors are stored to two kds of database, oe s for dgtal umbers, ad the other s for letters. he above feature vectors are used to tra SVMs wth RBF kerel (see Secto 5). I our expermets, 720 dmesoal feature vectors are put to SVMs, whch have bee traed successfully. he, whch character that a gve caddate should be ca be obtaed accordg to the outputs of SVMs. Whe all dgts ad letters o a umber plate are recogzed (or classfed), the recogto of the umber plate s complete.

7 5 Expermetal Results Comparso of SVMs Number Plate Recogto 7 Support vector maches our expermets are traed usg algorthms as show (Gu 997). Based o the approach we descrbed above, we dd expermets for dgtal umbers of 0 to 9 ad letters of A to Z. I our database, there are average 768 trag samples for character whch are segmeted from real mages of umber plates. Fgure 2 presets some of example of characters umber plates. We selected radomly oe thrd of them for trag ad the rest samples were used for testg. Fg.2. Segmeted characters he expermetal results are based o two methods, amely oe agast all ad oe agast oe. wo kerel fuctos that are lear kerel ad RBF kerel are used ad show below. Lear: K( x, x ) = x x 2 2 RBF: K ( x, x ) = exp( x x / 2σ ) ables 5. ad 5.2 show a comparso of usg the two methods. Also, we estmate the matchg rate usg dfferet kerel parameters σ ad cost parameters C. Matchg rate = Number of recogzed characters correctly/number of all testg characters. able 5. he expermetal results of characters (Dgts ad Letters) of umber plate (Oe agast all) We also report the trag tme, testg tme ad the percetage of support vectors the tables. All the expermets are performed o a Petum 4 PC wth 2.0GHz CPU. he trag tme ad testg tme crease wth the umber of trag sam-

8 8 Lhog Zheg, Xaga He ad om Htz ples. However, the classfcato accuracy does ot chage much. For further comparso, we also gve the expermetal results as show able 5.3 obtaed from well-kow database rs ad UCI (UCI). able 5.2. he expermetal results of characters (Dgts ad Letters) of umber plate (Oe agast oe) able 5.3. he expermetal results of rs ad UCI database (RBF) (Oe agast all) 6 Dscusso ad Coclusos he maor advatages of SVMs are that each SVM s a maxmal marg hyperplae a feature space bult usg a Kerel fucto, ad each SVM s based o frm statstcal ad mathematcal foudatos cocerg geeralzato ad optmzato theory. he trag for SVMs s relatvely easy. From the expermetal results, t s obvous that SVMs based o RBF kerel fucto perform better due to ts propertes descrbed above secto. he algorthm based o oe agast all gets hgher matchg rate tha method of oe aga oe. Due to ose cotaed the mage of real umber plates, the recogto rate s lower tha what obtaed some stadard database such as rs (Gu 997) ad UCI (UCI). But the followg cocluso stll holds. I oe agast oe method, each classfer must gve a label to a caddate o matter f t s correct or ot. herefore, may cases, error label formato s gve ad data are mstraed. he parameters after trag have lower credt. O the cotrary, however, oe agast all method shows better performace. For the faled cases our expermet, we otce that the amouts of every character s samples are ot evely our database. For example, character A owed much more samples tha other characters. Characters H ad L have smaller um-

9 Comparso of SVMs Number Plate Recogto 9 ber of samples our database. he parameters obtaed through trag are less powerful tha others whch were traed usg a bg amout of samples. Aother reaso s that the mages of these characters are much more blurred or dstorted tha the trag samples. hese characters are msclassfed to other smlar classes. However, compared wth earler results usg ductve Rule3 (Zheg, He, Wu ad Htz 2006) where the recogto accuracy rate s 7%, accuracy rates obtaed usg SVM s compettve ad better. Havg sad all above, SVMs ca be appled umber plate recogto successfully especally for heaver osy characters. Sce SVM has the hghest classfcato accuracy as a bary classfer, for further mprovemet of matchg rate, we should combe some other classfers together to make the umber of characters a group as small as possble. herefore, the overall matchg rate wll be deftely hgher tha other methods for umber plate recogto. Refereces Aksoy, M. S., Cagl, G. ad urker, A. K. (2000) Number-plate recogto usg ductve learg. Robotcs ad Autoomous Systems, Elsever, Vol.33, pp Crsta, N.ad Shawe-aylor, J. (2000) A troducto to support vector maches ad other kerel-based learg methods. Cambrdge Uversty Press. Dog, J., Sue, CY. ad Krzyzak, A. (2005) Algorthms of fast SVM evaluato based o subspace proecto IEEE Iteratoal Jot Coferece o Neural Networks, Vol. 2(3), pp Foody, G.M. ad Mathur, A. (2004) A relatve evaluato of multclass mage classfcato by support vector maches. IEEE rasactos o Geoscece ad Remote Sesg, Vol.42(6), pp Gu, S. R. (997) Support vector maches for classfcato ad regresso. echcal report. Image Speech ad Itellget Systems Research Group, Uversty of Southampto. UCI maches\data\uci.html Vapk, V. N. (999) he ature of statstcal learg theory. New York: Sprger. Zheg, L., He, X. ad L, Y. (2005) A comparso of methods for character recogto of car umber plates. Proc. of Iteratoal Coferece o Computer Vso (VISION 05), Las Vegas, pp Zheg, L. ad He, X. (2006) Number plate recogto based o support vector maches. Proceedg of IEEE AVSS 2006 coferece. ISBN-3: Zheg, L., He, X., Wu, Q. ad Htz,. (2006) Learg-based umber recogto o Spral Archtecture. Proceedg of IEEE ICARCV2006. Sgapore, pp