03 cntt nguyen thai nghe(17 30)

Tp ch Khoa hc Trng i hc Cn Th Phn A: Khoa hc T nhin, Cng ngh v Mi trng: 35 (2014): 17-30

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MT GII PHP TRONG XY DNG H THNG H TR GI XE THNG MINH Nguyn Thi Nghe1, Nguyn Vn ng1 v V Hng V1 1 Khoa Cng ngh Thng tin & Truyn thng, Trng i hc Cn Th

Thng tin chung: Ngy nhn: 27/05/2014 Ngy chp nhn: 29/12/2014 Title: A solution for building intelligent parking support system

T kha: H thng gi xe thng minh, nhn dng bin s xe, c trng Haar-like, m vch Keywords: Intelligent parking support system, motorcycle license plate recognition, Haar-like feature, barcode

ABSTRACT In this study, we propose a solution for building an Intelligent Parking Support System (IPSS). The IPSS uses three recognition techniques including automatic recognition for motorcycle license plate (using cascade of Boosting with Haar-like features and Support Vector Machines - SVM), barcode recognition, and semi-recognition via surveillance cameras. Experimental results show that the models work well in three stages. In the license plate area recognition stage, the model gets 99% of accuracy when we use 750 images for training and 243 images for testing. In the letter area recognition stage, the model achieves 95.88% of accuracy when we use 11866 and 4755 images for training and testing, respectively. In the last stage, we train the SVM model on 2603 records and test it on 1550 records. The outcome result is the accuracy of 98.99%. This is a full-fledged system and can be applied in practice.

TM TT Bi vit ny gii thiu mt gii php trong xy dng H thng h tr gi xe thng minh - IPSS. Nhm tng an ton cho bi xe, IPSS kt hp cc k thut nhn dng gm: Nhn dng v rt trch bin s t ng (dng gii thut Boosting phn tng cng vi c trng Haar-like v sau l gii thut my hc SVM), nhn dng m vch, v nhn dng bn t ng qua cc camera quan st. Thc nghim cho thy kt qu nhn dng kh tt c ba giai on: nh v bin s t chnh xc 99% khi dng 750 nh hun luyn v 243 nh kim th. nh v k t chnh xc 95.88% khi dng 11866 nh cho hun luyn v 4755 nh kim th. Phn loi k t bng gii thut SVM chnh xc 98.99% khi s dng 2603 phn t cho hun luyn v 1550 phn t cho nh gi. H thng c ci t hon chnh v c th a vo ng dng trong thc t.

1 GII THIU Do mt dn s nc ta phn b kh ng

ti cc thnh ph ln trong khi cc phng tin giao thng cng cng cn cha pht trin kp nn bng n cc phng tin giao thng c nhn, c bit l xe my. Chnh v th cng tc qun l, trng coi, kim sot xe my, c bit l pht hin cc hnh vi ca k gian,... tn nhiu thi gian v cng sc.

Trong thc t, c nhiu nhm nghin cu v thng mi ha bi ton xy dng h thng h tr gi xe, chng hn nh nhm Mt thn thuc Trung tm Cng ngh M phng - Hc vin K thut qun s cho ra i sn phm H thng qun l bi xe thng minh ng dng cng ngh nhn dng bin s kt hp vi cng ngh th nm 2006; Cng ty c phn u t pht trin phn mm Bin Bc nghin cu v pht trin thnh cng phn


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mm STM01 trong qun l phng tin v pht hin vi phm giao thng nm 2010; Bi gi xe thng minh ca cng ty AIO , h thng ny c tch hp my nhn dng vn tay (t h thng chm cng) do ch c chnh ngi a xe vo mi c th ly xe ra. iu ny c th gy bt tin, chng hn nh ai mn nh xe ngi khc hoc cc siu th, khu mua sm khi ngi a xe vo l chng, cn ngi ly xe ra l v do ngi chng bn khing hng ha,...; Hay h thng PTP_SP : H thng gi xe thng minh ca cng ty Vng Thnh Pht ang c ci t kh nhiu ti cc siu th hin nay. Tuy nhin, h thng ny ch s dng camera ghi li hnh nh v bin s xe ca ngi gi h tr cho nhn vin gi xe quan st ch cha nhn dng t ng, v do vy vic nhn dng vn phi cn nhn vin thc hin. Mt s nghin cu khc cng tp trung trn lnh vc bn t ng hoc ch dng li mc kim tra th nghim gii thut.

T nhng thc t trn, vic nghin cu v xy dng mt h thng h tr gi xe thng minh - tn gi tt l IPSS (Intelligent Parking Support System) c th ng dng trong thc t vn l cn thit. c bit trong nghin cu ny, chng ti kt hp cc k thut gm vic nhn dng bin s, nhn dng k t, phn loi k t bng gii thut my hc SVM, s dng cng ngh barcode kim tra th xe vo ra. Bn cnh phng php t ng, nhm tng an ton ti a, h thng ny cng h tr lu tr v truy xut li nh bin s xe, nh khun mt ca ngi gi xe nhn vin c th so snh i chiu.

2 NHNG NGHIN CU LIN QUAN TRONG LNH VC NHN DNG

Bi ton pht hin cc i tng trong nh nh gng mt ngi, n ci (smile),... rt c quan tm. Cc nh sn xut my nh nh Canon, Nikon, Samsung, cng tch hp cc gii thut nhn dng vo trong sn phm ca mnh v pht hin mt ngi. Trong nhng nghin cu trc y v lnh vc nhn dng bin s xe, cc phng php c th trnh by trong 3 nhm: phng php hnh thi hc, phng php so khp mu, v phng php my hc (J.-W. Hsieh et al., 2002, K.-I. Kim et al., 2002).

Vi phng php bin i Hough, V. Kamat v S. Ganesan (1995) xut nh sau: S dng bin i Hough pht hin cc ng thng song song c xem nh cc ng c vin ca bin s. Sau , cc rng buc hnh hc ca bin s c p dng kim tra xem cc ng vin ny c phi

l bin s hay khng. Bn cnh mt s rng buc chun nh chiu cao/rng ca cc k t phi bng nhau, tm cc k t phi nm trn mt ng thng, chiu cao ca mi k t phi ln hn phn na chiu cao bin s trch xut c c J.-W. Hsieh (2002) gii thiu. Mt tip cn khc l s dng thng k v ng bin v cc ton t hnh thi trch xut cc ng vin (bin s xe), sau loi b cc ng vin khng ng bng cch kim tra cc rng buc. Nhng phng php ny c u im l khng ph thuc vo mu sc ca bin s xe, tuy nhin chng ph thuc kh nhiu vo bc trch c trng bin cnh (cc on thng ng vin thu c thng ngn hn nhiu so vi chiu dc cng nh chiu ngang ca bin s).

Trong phng php so khp mu, cc mu chun ca bin s xe s c nhn dng trc hoc nhn dng cc tham s thng qua mt hm. T mt nh a vo, tnh cc gi tr tng quan so vi cc mu chun. Thng qua cc gi tr tng quan quyt nh c tn ti bin s xe trong nh hay khng. Phng php ny n gin, d ci t, tuy vy n cha hiu qu khi t l, t th, v hnh dng thay i.

phng php my hc, K.-I. Kim et al. (2002) gii thiu phng php pht hin bin s xe bng cch p dng my hc SVM ln trn cc ng vin c xem nh l cc vng c kt cu ri rc (discriminatory texture). Khuyt im ca phng php ny l ph thuc nhiu vo hnh dng v v ngoi ca bin s, do n khng hiu qu trong iu kin nhiu, thiu sng v thay i php quay. khc phc cc im bt li trn, Q-N. Tran et al. (2008) xut mt nhn dng bng cch kt hp nhng im mnh ca vect m t SIFT (Scale-Invariant Feature Transform) v my hc SVM thng qua vic trch cc c trng ca bin s xe cng nh khng phi bin s xe a vo cho gii thut SVM hc, t c th phn loi cho cc i tng mi. Phng php ny khc phc c cc thch thc chnh trong phn loi nh nh bin i t l, thay i gc nhn, php quay, thay i v sng, nhiu. Tuy nhin, n c phc tp cao v thi gian thc hin kh lu.

3 C TRNG HAAR-LIKE, GII THUT BOOSTING PHN TNG V M HNH SVM

3.1 c trng Haar-like Cc c trng Haar-Like (P.-A. Viola v M-J.

Jones, 2001) l nhng hnh ch nht c phn thnh cc vng khc nhau nh biu din trong Hnh 1:


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a. c trng cnh

b. c trng ng

c. c trng xung quanh tm

d. c trng ng cho Hnh 1: Cc c trng Haar-Like

Gi tr ca c trng Haar-like c xc nh bi chnh lch gia tng gi tr pixel mc xm nm trong vng en so vi vng trng:

f(x)=Tngvng en (cc mc xm ca pixel) Tngvng trng (cc mc xm ca pixel)

Hnh 2: Phng php tnh Integral Image Nh vy tnh cc gi tr ca c trng Haar-

like, ngi ta phi tnh tng ca cc pixel trn nh. Nhng tnh ton cc gi tr ca cc c trng Haar-like cho tt c cc v tr trn nh i hi chi ph tnh ton kh ln, khng p ng c cho ng dng i hi tnh run-time. Do , Viola v Jones (2001) a ra mt khi nim gi l Integral Image - l mt mng hai chiu vi kch thc bng vi kch thc ca nh cn tnh cc c trng

Haar-like, vi mi phn t ca mng ny c tnh bng cch tnh tng ca cc im nh pha trn (dng - 1) bn tri (ct - 1) ca n. Bt u t v tr trn bn tri, n v tr di bn phi ca nh. Vic tnh ton ny ch da trn php cng s nguyn, do tc c ci thin ng k.

Hnh 3: Tnh gi tr mc xm ca vng D trn nh

Sau khi tnh c Integral Image, vic tnh tng cc gi tr mc xm ca mt vng bt k no trn nh thc hin theo cch sau: Gi s ta cn tnh tng cc gi tr mc xm ca vng D nh trong Hnh 3, ta c th tnh: D = A + B + C + D (A+B) (A+C) + A Vi A + B + C + D chnh l gi tr ti im P4 trn Integral Image, tng t nh vy A+B l gi tr ti im P2, A+C l gi tr ti im P3, v A l gi tr ti im P1. Vy ta c th vit li biu thc tnh D:

D = (x4, y4) (x2, y2) (x3, y3) + (x1, y1) A+B+C+D A+B A+C A

3.2 M hnh phn lp phn tng (Cascade Classifier)

Trong m hnh phn lp phn tng, mi tng l mt b phn lp. Mt mu c pht hin l i tng th n cn phi i qua ht tt c cc tng ca m hnh, cc b phn lp tng sau c hun

luyn bng nhng mu m b phn lp trc n nhn dng sai. M hnh cascade cho nhn dng i tng c biu din trong Hnh 4. i tng trong nghin cu ny l nh bin s xe giai on nh v bin s v l nh k t giai on nh v k t.


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Hnh 4: M hnh phn lp phn tng

Thut ton ny tin hnh phn ra thnh cc giai on (stage), cc vng ng vin phi t ngng ng (threshold) ca tt c cc giai on c xc nh l c cha i tng. S lng giai on v ngng quyt nh bi d liu hc v cc tham s khc do ngi dng ch nh. c th nhn dng thut ton tin hnh ly mu t 2 tp d liu: tp nh dng bao gm nh c cha i tng v tp nh m khng cha i tng. Chuyn cc nh ny sang nh mc xm v tin hnh hc da trn cc nh ny. D liu hc cng nhiu th nhn din cng chnh xc, bao gm c nh dng v m, b li th thi gian hun luyn lu hn. Nu vng nghi ng c xc nh ng l i tng th thut ton tr v nhng khung hnh ch nht (v c th c nhiu i tng trong 1 hnh). M hnh phn lp phn tng kt hp vi cc c trng Haar-like c xy dng nhm rt ngn thi gian x l, gim thiu t l nhn dng sai ca b nhn dng (bn c quan tm xin tham kho thm trong ti liu ca P.-A. Viola v M-J. Jones (2001).

3.3 Gii thut my hc Support Vector Machines (SVM)

SVM (C. Corinna and V. Vapnik, 1995) c p dng thnh cng trong rt nhiu ng dng nh nhn dng vn tay, nhn dng mt ngi, phn loi bnh ung th,. . . Trong nghin cu ny, chng ta quan tm ti SVM l mt phng php phn lp tuyn tnh (linear classifier), vi mc ch xc nh mt siu phng (hyperplane) ti u phn tch hai lp ca d liu ngha l cho php chia cc im (phn t) d liu thnh hai phn sao cho cc im cng mt lp nm v mt pha ca siu phng ny. i vi tp d liu ca bi ton c nhiu lp chng ta c th a v bi ton 2 lp bng k thut 1-tt c

(one vs rest) hoc 11 (one vs one). V d, phn lp nh phn tuyn tnh, cho m phn t (im) d liu trong khng gian n chiu: {(xi, yi)}, i={1. .m}, vi cc thuc tnh xi Rn v nhn (lp) yi{1,-1}. Sau qu trnh ti u tm ra cc tham s (cc vc t h tr - support vectors (vi s lng NSV) v cc nhn t Lagrange i ), SVM phn lp phn t d liu mi n x nh sau:

SVN

iiii bxxysignxpredict

1)(

Trong :

4 XUT PHNG PHP NHN DNG V RT TRCH K T TRONG BIN S XE

Chng ti xut quy trnh nhn dng v rt trch bin s qua cc bc chnh nh m t trong Hnh 5. , sau khi h thng nhn c nh chp t camera, h thng s nh v v trch ra vng cha bin s. T vng cha bin s ny, thc hin tng t trch ra nh cc k t c trong vng bin s . Cc nh k t ny sau c chuyn sang dng nh phn lm u vo cho b phn lp SVM. Sau cng, gii thut SVM s phn loi cc gi tr nh phn ny thuc vo cc k t tng ng no (0..9, A..Z).

nh .. C1 CN i tng ng ng ng

Khng phi i tng

Sai Sai Sai


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Hnh 5: Quy trnh nhn dng v phn lp bin s xe

Sau y, chng ti s m t chi tit cch thc hin cho mi bc trong hnh trn.

4.1 nh v vng bin s xe Giai on ny s dng c trng Haar-like v

Boosting phn tng (mi b phn lp trong Hnh 3 l mt m hnh Boosting vi b phn lp yu l Cy quyt nh) nh v vng bin s xe, sau trch nh bin s ra nh minh ha trong Hnh 6. Gii thut nh v ny c sn trong th vin EmguCV1, vn ta cn thc hin l tin v hu x l nh c c vng bin s mong mun. Chi tit v cch thc hin c trnh by qua cc bc di y.

Hnh 6: nh v v trch nh bin s xe

Bc 1: Chun b tp d liu hun luyn gm 2250 nh mu. Trong , 750 nh cha bin s xe (tp nh ny l tp nh dng - positive) v 1500 nh khng cha bin s xe (hnh bt k nh phong cnh, vt dng,.. Tp nh ny cn gi l tp nh m (negative) hay nh nn (background)). Lu : Hai tp nh ny nn cng kch thc (trong nghin cu ny l 640 x 480 pixels) v nn xoay nh theo nhiu gc vt cn cc trng hp thc t. 1 http://www.emgu.com/

Bc 2: Xc nh ta ca cc vng cha bin s xe, sau lu ton b chng vo tp tin (v d location.txt) vi ni dung: / x11 y11 w11 h11 x12 y12 w12 h12 / x21 y21 w21 h21

Vi: x11, y11 l ta bin s xe; w11, h11 l chiu rng v chiu cao bin s nh minh ha trong Hnh 7.

V d: D:/bienso/1.jpg 1 132 112 303 216 D:/bienso/2.jpg 1 164 122 288 209

giai on ny, chng ti pht trin mt b cng c c nh, click-and-drag chn vng bin s v xc nh ta i tng (sau lu li vo file location.txt nh trnh by trn).

Hnh 7: Xc nh ta vng cha bin s xe


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Bc 3: To cc mu dng cho hun luyn (s dng chng trnh opencv_createsamples2 trong th vin EmguCV), lu li vo tp tin vector dng hun luyn bc tip theo. C php:

opencv_createsamples -vec vec.vec -info D:\bienso\location.txt -num 750 -w 40 -h 30

Trong : vec vec.vec: file vector ouput c lu vi

tn vec.vec. y l tp tin vect cha nh thumbnail vi kch c c ch ra trong tham s width v height ca tp tin location.txt. Da vo file ny m chng trnh s tm ra cc c trng haar-like ca i tng, t to b nhn din da trn c trng ny.

info D:\bienso\location.txt: tp tin cha ta bin s to Bc 1.

num 750: s nh dng (bin s) l 750. w 40 -h 30: tham s xc nh kch thc nh

ti thiu. Nu kch thc bin s nh hn 40x30 th chng trnh s cho rng khng c bin s trong nh.

Bc 4: To tp tin ch mc cc nh khng cha bin s xe (v d bg.txt), tp tin ny n gin l cha cc ng dn v tn nh background (nh m nh bt k khng phi hoc khng cha bin s xe v c cng kch thc 640 x 480 pixels vi nh cha bin s xe), v d:

/ /

Bc 5: Hun luyn m hnh. S dng chng trnh opencv_haartraining (i km vi EmguCV) hun luyn, lu tp tin m hnh vi tn bienso.xml. C php nh sau:

opencv_haartraining -data D:\bienso -vec vec.vec -D:\bienso\bg.txt -npos 750 -nneg 1500 -nstages 20 -mem 2000 -w 40 -h 30 -nonsym -minhitrate 0.995 -maxfalsealarm 0.5

Trong : data D:\bienso: ng dn th mc cha

cc file thng s ng ra c cp nht ca tng hun luyn (cascade). Khi hun luyn chng trnh

2 http://docs.opencv.org/doc/user_guide/ug_traincascade.html

s to ra cc th mc t 0 nstages. Khi hun luyn xong s to ra file bienso.xml cha m hnh s dng v sau.

vec vec.vec: cha ng dn n file vector to ra Bc 3.

bg D:\bienso\bg.txt: ng dn n file cha danh sch cc nh nn (nh m) to Bc 4.

npos 750: s lng nh dng (bin s). nneg 1500: s lng nh m (nh nn). nstages 20: s lng giai on (stage)

hun luyn. mem 2000: dung lng b nh RAM cn

cho qu trnh hun luyn (MB). nonsym: khai bo cc i tng hun luyn

l khng c tnh i xng. minhitrate 0.995: l t l d on ng

ti thiu. maxfalsealarm 0.5: t l d on sai ti a. 4.2 nh v k t Hon ton tng t nh giai on nh v

bin s, ta thc hin vic nh v v trch nh cc k t trong bin s xe nh minh ha Hnh 8 (lu : sau bc ny ta mi ch nhn c cc k t di dng nh).

Hnh 8: nh v v trch nh k t trong bin s xe

Chng ti s dng d liu hun luyn trong giai on ny gm 5200 nh mu. Trong , 1500 nh cha nh k t bin s xe v 3700 nh khng cha k t bin s xe (hnh phong cnh, vt dng). Cc bc thc hin hon ton tng t giai on nh v bin s. Sau khi nh v c nh k t, tch v chuyn chng v kch thc 20x48 pixels nh minh ha trong Hnh 9. Vi mi nh k t thu c, chng ti xut chuyn chng v gi tr nh phn lm tp d liu u vo cho b phn lp SVM.

Tch nh k t


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Hnh 9: nh k t sau khi tch

4.3 Chuyn nh k t sang dng nh phn bc ny, chng ti chuyn cc nh k t

(20x48) thnh gi tr nh phn biu din tng ng cho nh, nh minh ha trong Hnh 11. Gii thut c chng ti s dng y rt n gin l vi mi im nh, nu mc sng (intensity) ca n ln hn mt ngng no (v d, trong nghin cu ny chng ti s dng gi tr trung bnh mc xm ca mi nh lm gi tr ngng) th s chuyn thnh 1, ngc li l 0.

Tuy nhin, trong thc t s c lc nhng k t trong bin s khng thng ng m nghing v mt bn no , do tp d liu phi bao gm nhiu gc xoay khc nhau. Qua kho st thc t cho thy rng gc nghing nm trong khong t -10 n 10 nh trong Hnh 10.

Hnh 10: Cc gc xoay ca 1 k t

Hnh 11: Chuyn nh k t thnh ma trn nh phn 20x48. Hnh minh ha cho nh k t D, tuy nhin

ta c th chuyn ton b th mc nh


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Sau khi chuyn nh k t sang dng nh phn, ty theo b phn lp (classification) c s dng m ta s chuyn thnh nh dng u vo tng ng ca b phn lp . Trong nghin cu ny, chng ti s dng LibSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) vi nh dng d liu u vo nh sau: :: : :

Trong , ct u tin l nhn (lp ch cn phn loi target class), tip theo l cc v tr c gi tr. V d trong Hnh 11, cc s nh phn kch thc 20x48 c 960 v tr v ch D c biu din theo nh dng ca LibSVM nh sau: 13 23:1 24:1 869:1 870:1

Vi s 13 tng ng k t D nh quy c trong bng di y:

K t A B C D E F G H K L M N P R S T U V X Y Z S tng ng 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Thc hin tng t cho tt c cc nh k t thu

c phn 4.2 (minh ha trong Hnh 9) ta thu c tp d liu dng hun luyn m hnh SVM.

4.4 Xy dng m hnh SVM S dng b d liu thu c phn 4.3 trc,

hun luyn m hnh SVM (c th s dng cng c LibSVM) vi cc tham s mc nh. Trong tng lai, chng ti s ci tin kt qu hun luyn bng cch tm kim cc siu tham s (hyper-parameters) tt nht, nh gi tr C, hm kernel, gi tr gamma,..

4.5 Nhn dng v phn loi cho mt nh bin s xe mi n

Khi c mt nh bin s xe mi n, ta s s dng cc m hnh c nh v bin s, nh v k t, chuyn k t v dng nh phn v sau cng l dng SVM nhn dng (phn loi) cc k t c trong bin s ny.

5 XY DNG H THNG Sau cc bc hun luyn cc m hnh, cng

vic cn li l xy dng h thng hon chnh tch hp cc m hnh ny vo.

Tng t nh bt k mt h thng thng tin qun l no, cc chc nng chnh trong h thng ny bao gm:

Qun l xe vo/ra (lu tr hnh nh bin s, khun mt ca ngi gi xe, lu tr cc k t trong bin s xe sau nhn dng, thi gian vo/ra..).

Qun l th xe (to th xe km m vch, in th, bo mt, bo h,..).

Qun l thng tin nhn vin (gm lch trc). Qun l ti khon h thng (quyn, nhm..). Tm kim xe theo cc thng tin nh bin s

xe, thi gian vo/ra Thng k lu lng xe ra/vo, doanh

thu, thi gian lm vic ca nhn vin theo khong thi gian...

5.1 S tng th ca h thng S tng th ca h thng c biu din

trong Hnh 12. , khi xe vo s dng ti vch quy nh, lc ny nhn vin s qut m vch trn th gi xe lu li s trn th. Cng lc , camera th nht chp nh gng mt ca ngi gi xe, camera th 2 s chp nh bin s xe, c 2 nh ny u c h thng lu li. nh bin s xe s c nhn dng v chuyn thnh cc k t tng ng. Nh vy, cc thng tin quan trng c h thng ghi li l s th xe, nh gng mt ngi gi xe, nh bin s xe, k t bin s, v thi gian vo/ra.


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Hnh 12: S tng th ca h thng

Khi xe ra cng dng ngay v tr quy nh, nhn vin s qut m vch trn th nhn t ngi gi xe, h thng t ng truy xut li cc thng tin khi xe vo so snh i chiu, nu bin s ging nhau th chng trnh s thng bo ng v cho xe ra. Ngc li s cnh bo nhn vin so snh i chiu cc nh lu.

i vi xe p, do khng c bin s nn thuc tnh ny s khng c lu li. Cc thng tin khc th tng t nh trng hp ca xe my.

Cng cn lu rng m hnh ny l gii php bn t ng, do vy vn phi c nhn vin gi xe. Gii php ny h tr ti a cho nhn vin nhm gim sai st trong nhn dng bng mt qua tnh nng nhn dng bin s t ng v a ra cc li cnh bo ch khng cng nhc trong vic bt lm hay tha lm.

Ngoi ra, h thng khng hon ton ph thuc vo th gi xe barcode m phi qua cc i chiu

nh i chiu t ng cho phn bin s (module trong Hnh 12), i chiu qua quan st hnh nh (gng mt, qun o, hnh bin s,.. module ) ca ngi gi xe (do nhn vin thc hin) do vic in th gi khng phi l tr ngi ln cho an ton ca h thng. Tuy nhin, trong tng lai chng ti s hng n s dng th t - cng ngh bo mt tt hn nhng gi thnh cng t hn. So vi cc h thng hin ti cc siu th, bnh vin,... (ch c module ) th h thng IPSS ny hon ton kh thi.

5.2 Lc c s d liu (CSDL) Do mi lt xe vo/ra s c 4 nh c lu li,

chng ti chn gii php ch lu ng dn n nh trong m khng lu trc tip nh vo vo CSDL. Cc bc nh s c lu ring trn a do kch thc CSDL ci thin ng k trong khi tc truy xut cng mc chp nhn c. Lc CSDL minh ha trong Hnh 13.

1

2

3


26

Hnh 13: Lc CSDL

5.3 Thit k giao din gi/tr xe Chng ti chn gii php 8 khung hnh cho

giao din khi xe vo/ra nh minh ha trong Hnh 14. Trong , khung 1&2, khung 7&8 l hnh nh trc tip t camera. Khi xe vo, hnh nh t camera khung 1&2 s c lu li v hin th trn khung

3&4. Khi xe ra, h thng s load hnh nh lc xe vo v hin th khung 5&6. Giao din ny ph hp vi trng hp mt cng vo ra. Nu trin khai h thng di dng mt cng vo v mt cng ra c lp nhau, giao din ny c th ty bin li cn 4 khung hnh cho ph hp.

Hnh 14: Giao din chnh ca phn qun l xe vo/ra

5.4 Ci t v trin khai h thng H thng c xy dng trn nn tng .NET

(VB.NET), h qun tr c s d liu SQL Server, th vin EmguCV v LibSVM.

S trin khai h thng minh ha trong Hnh 15. Trong , cc thit b cn thit gm: 01 b my tnh (mn hnh cng ln cng tt, t nht l 19), 01 my qut m vch, 04 camera (hay webcam phn gii cao), cc ro chn v thit b bo v khc.

Camera

Camera

Camera

Camera


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Hnh 15: S trin khai h thng

6 KT QU T C 6.1 chnh xc ca cc m hnh Trong giai on nh v vng bin s, chng ti

s dng 750 nh bin s cho hun luyn v 243 nh cho kim th. Kt qu chnh xc t 99%. Trong giai on nh v k t, 11866 nh c dng hun luyn v 4755 nh c dng kim th, kt qu chnh xc t 95.88%. Giai on phn loi k t bng SVM t chnh xc 98.99% khi dng 2603 phn t cho hun luyn v 1550 phn t cho kim th. Cc thc nghim c

thc hin theo nghi thc kim tra cho 3 ng (3-fold cross validation).

Ngoi ra, sau khi xy dng h thng xong chng ti cng kim tra li chnh xc ton b quy trnh nhn dng ca h thng (gm nh v v nhn dng/phn lp bin s) trn 215 nh bin s mi. Kt qu cho thy t l nhn dng ng hon ton (nh v v phn lp) t 190/215 nh (tng ng 88.37%) ln chp th nht ca camera. C 25/215 (11.62%) bin s c nh v ng nhng phn lp sai 1 k t (a phn l sai do iu kin khch quan nh bin s qu c do vy k t trn bin s qu m hoc b chi nh sng, hoc mt vi trng hp cc k t c hnh thi gn ging nhau nh H v N; E v F). khc phc tnh trng trn, trn form giao din h thng (nh minh ha trong Hnh 16) chng ti ci t chc nng nhn dng li thng qua vic n phm nng hoc click vo nt c tip cho php ngi dng kch hot li vic chp v nhn dng bin s. Kt qu cho thy 25 nh b nhn dng sai ln chp th nht gim xung cn 10 nh ln 2 v khng cn nh no b sai ln chp th 3. Nh vy, trong thc t, i vi nhng bin s b nhn dng sai, ngi dng s n phm h thng chp v nhn dng li (thao tc n phm ny khng qu 3 ln v ch mt khong 1 giy/ln do gii php ny hon ton kh thi).

6.2 Mt s giao din minh ha Giao din chnh khi xe vo ra c minh ha

trong Hnh 16, tng t nh trnh by trong phn thit k mc 5.3.

Hnh 16: Giao din gi/tr xe


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Giao din minh ha cho phn qun l th xe km m vch c trnh by trong Hnh 17. , ngi s dng c th ty bin mu sc, ni dung in

trn th gi xe cho ph hp vi tng n v c trin khai.

Hnh 17: Giao din in th xe vi m vch

Giao din minh ha cho phn tm kim thng tin xe vo/ra theo tiu ch no (bin s, thi im,..) c trnh by nh trong Hnh 18. Thng

tin c truy xut bao gm thi im, bin s dng k t, nh ngi gi, nh bin s do vy rt tin li trong vic qun l.

Hnh 18: Giao din Tm thng tin xe vo/ra


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Ngoi ra, h thng cn h tr nhiu tnh nng khc tng ng vi 2 nhm ngi dng chnh l admin v nhn vin.

Nhm nhn vin c cc quyn nh qun l xe ra - vo, bo mt th, bo h th, qun l mt khu ng nhp, ty chn camera, ty chn th mc lu nh, tm kim xe vo ra theo cc tiu ch, thng k...

Nhm admin c tt c quyn ca nhn vin v thm cc quyn nh: cp nht thng tin nhn vin, chm cng, qun l gi tin gi cho tng loi xe, in th xe...

7 KT LUN V HNG PHT TRIN Chng ti xut gii php xy dng h

thng h tr gi xe thng minh IPSS. y l h thng kt hp c ba k thut nhn dng nhm tng an ton ti a trong qun l bi xe, gm: Nhn dng bin s, nhn dng m vch, v nhn dng bn t ng qua cc camera quan st. Ngoi ra, IPSS cn h tr nhiu tnh nng khc nh in th gi xe (km m vch), tm kim, thng k, qun l lch trc, IPSS c xy dng trn nn tng .NET + SQL Server, th vin EmguCV (OpenCV) + LIBSVM.

Nghin cu s tip tc c hon thin thng qua vic ci thin chnh xc ca m hnh nh v k t. Hin ti, do b d liu hun luyn cn cha ln nn mt s k t gn ging nhau vn cn b nhn dng sai, nh E v F, N v H, 1 v 7.

LI CM T Nghin cu ny c ti tr bi ti NCKH

cp Trng HCT m s T2014-08 v Phng X l d liu thng minh Khoa CNTT&TT, HCT. Nhm tc gi chn thnh cm n cc gp ca TS. Phm Nguyn Khang.

TI LIU THAM KHO 1. Paul A. Viola and Michael J. Jones, 2001.

Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE Conference on Computer Vision and Pattern Recognition, pages 511-518.

2. Paul A. Viola and Michael J. Jones, 2004. Robust real-time face detection. International Journal of Computer Vision, pages 137-154.

3. Freund, Y. and Schapire, R., 1995. A decision-theoretic generalization of on-line

learning and an application to boosting. Computational Learning Theory, pp. 23-37.

4. Q-N. Tran, T-N. Do, F. Poulet, N-K. Pham, 2008. Vehicle license plate classification. in proc. The National conference in computer science, pp. 79-85.

5. V. Kamat and S. Ganesan, 1995. An efficient implementation of the Hough transform for detecting vehicle license plates using dsps. In RTAS 95: Proceedings of the Real-Time Technology and Applications Symposium, page 58, Washington, DC, USA. IEEE Computer Society.

6. J-W Hsieh, S-H Yu, and Y-S Chen, 2002. Morphology-based license plate detecttion from compex scenes. In Proceedings of the International Conference on Pattern Recognition, 3:30-176, IEEE Press, Washington, USA.

7. K. I. Kim, K. Jung, and J. H. Kim, 2002. Color texture-based object detection: An application to license plate localization. In proceedings of the First International Workshop on Pattern Recognition with Suppor Vector Machines, Springer-Verlag, London, UK, pp. 293-309.

8. L. Dlagnekov, 2005. Car license plate, make, and model recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA.

9. H. Mahini, S. Kasaei, F. Dorri, and F. Dorri, 2006. An efficient features based license plate localization method. In ICPR (2), pages 841844. IEEE Computer Society.

10. C. Schmid and R Mohr, 1997. Local gray value invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5): 530535.

11. D-G. Lowe, 1999. Object Recognition from Local Scale Invariant Features. In Proceedings of the 7th International Conference on Computer Vision, Kerkyra, Greece, pp. 11501157.

12. D-G. Lowe, 2004. Distinctive Image Features from Scale Invariant Keypoints. In International Journal of Computer Vision, pp. 91110.


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13. M. Turk and A. Pentland, 1991. Face recognition using eigenfaces, IEEE Conference on Computer Vision and Pattern Recognition, pp. 586591.

14. Schneiderman H. and Kanade T., 2000. A statistical method for 3D object detection applied to faces and car, In International Conference on Computer Vision.

15. Cortes, Corinna and Vladimir Vapnik, 1995. Support-Vector Networks. Machine Learning 20: 273297.

16. Thanh Ngh, 2011. Khai m d liu - Minh ho bng ngn ng R. Nh xut bn i hc Cn Th.

03 cntt nguyen thai nghe(17 30)

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