Real-time Vision-based Multiple Vehicle Detection and Tracking for Nighttime Traffic Surveillance

Transcription

1 Proceedngs of he 009 IEEE Inernaonal Conference on Sysems, Man, and Cybernecs San Anono, TX, USA - Ocober 009 Real-me Vson-based Mulple Vehcle Deecon and Tracng for Nghme Traffc Survellance Yen-Ln Chen, Bng-Fe Wu,*, and Chung-Ju Fan Dep. of Compuer Scence and Informaon Engneerng, Naonal Tape Unversy of Technology, Tape, Tawan e-mal: ylchen@cse.nu.edu.w Deparmen of Elecrcal and Conrol Engneerng, Naonal Chao Tung Unversy, Hsnchu, Tawan e-mal: *bwu@cssp.cn.ncu.edu.w Absrac Ths sudy presens an effecve sysem for deecng and racng movng vehcles n nghme raffc scene for raffc survellance. The proposed mehod denfes vehcles based on deecng and locang vehcle headlghs and allghs by usng he echnques of mage segmenaon and paern analyss. Frs, o effecvely exrac brgh obecs of neres, a fas brgh-obec segmenaon process based on auomac mullevel hsogram hresholdng s appled on he nghme road-scene mages. Ths auomac mullevel hresholdng approach can provde robusness and adapably for he deecon sysem o be operaed well under varous llumnaon condons a ngh. The exraced brgh obecs are processed by a spaal cluserng and racng procedure by locang and analyzng he spaal and emporal feaures of vehcle lgh paerns, and hen denfyng and classfyng he movng cars and moorbes n he raffc scenes. Expermenal resuls demonsrae ha he proposed approach s feasble and effecve for vehcle deecon and denfcaon n varous nghme envronmens for raffc survellance. Keywords Inellgen ransporaon sysems, vehcle deecon, vehcle racng, nghme survellance, raffc survellance. I. INTRODUCTION Deecng and recognzng movng vehcles n raffc scenes for raffc survellance, raffc conrol, and road raffc nformaon sysems s an emergng research area for Inellgen Transporaon Sysems. The nformaon of movng vehcles may be obaned from he sensor such as loop deecors, sl sensors, or cameras. Among he abovemenoned sensors, camera-based sysems can provde much more nformaon for raffc analyss, such as raffc flow, vehcle classfcaon, and vehcle speed. Due o he progress on he reducng cos and growng compung power of he hardware, he vson-based echnologes has become he popular soluons for raffc survellance and conrol sysems. To-dae, many researchers have developed valuable echnques for deecng and recognzng vehcles and obsacles from mages of raffc scenes []- [0]. By monorng he llumnaon change n some pre-specfed deecon regons, he echnques based on vrual sl [] or vrual loop deecors [] can rapdly deec he movng vehcles when passng hese regons. However, such mehods are lmed o oban he number of passng vehcles passng hrough he gven deecon regons, and are dffcul o apply on vehcle classfcaon, vehcle speed deecon, and vehcle moon analyss. To more effcenly oban he raffc nformaon of movng vehcles, framedfferencng based echnques are appled for segmenng he movng vehcles from moonless bacground scenes by change deecon or some sascal models. In [3][4], spaalemporal dfference feaures are appled for segmenng he movng vehcles, whle he mehods n [5][6][7][9] ulze he bacground subracon based echnques for exracng movng vehcles. These mehods can effcenly apply on he dayme raffc scenes wh saonary and unchanged lgh condons. However, under condons of bad-llumnaed a nghme, he bacground scenes are subsanally affeced by lghng effec of movng vehcles, so ha hose obvous cues of bacground models whch are effecve for vehcle deecon durng dayme become nvald. Thus, mos of he abovemenoned frame-dfferencng based echnques may no wor well under such nghme raffc envronmens. A ngh, as well as under darly llumnaed condons n general, he only salen feaures of movng vehcles are her headlghs and allghs. To deec salen obecs for nghme raffc survellance, Beymer e al. [8] presened a feaure-based echnque by exracng and racng he se of corner feaures of movng vehcles nsead of her enre regons, and can wor under boh dayme and nghme raffc scenes. However, hs echnque suffers hghly compuaonal cos. Huang e al. [0] proposed a mehod based on bloc-based conras analyss and ner-frame change nformaon. Ths conras-based mehod can effecvely deec oudoor obecs n a gven survellance area usng a saonary camera. However, he conras and ner-frame change nformaon are sensve o lghng effec caused by headlgh beams of movng vehcles, and resul n many erroneous resuls on vehcle deecon. In hs sudy, we propose an effecve nghme vehcle deecon and racng approach for denfyng and classfyng movng vehcles by locang and analyzng spaal and emporal feaures of her vehcle lghs for raffc survellance. Ths proposed sysem comprses he followng processng sages. Frs, a fas brgh-obec segmenaon process based on auomac mullevel hsogram hresholdng s performed o exrac pxels of lghng obecs from he capured mage /09/$ IEEE /09/$ IEEE 345

2 sequences of nghme raffc scenes. These lghng obecs are hen grouped by a spaal cluserng process o oban groups of vehcle lghs of poenal movng cars and moorbes. Nex, a feaure-based vehcle racng and denfcaon process s appled o analyze he spaal and emporal nformaon from hese poenal vehcle lgh groups from consecuve frames, and o refne he deecon resuls and correc for groupng errors and occlusons. Acual vehcles and her ypes can hus be effcenly deeced and verfed from hese raced poenal vehcles o oban he raffc flow nformaon n he road scenes. Expermenal resuls demonsrae ha he proposed approach s feasble and effecve for vehcle deecon and denfcaon n varous nghme envronmens for raffc survellance. II. LIGHTING OBJECT EXTRACTION The npu mage sequences grabbed from he vson sysem, whch s mouned on he elevaed plaform of he hghway and urban roads. The mage sequences are grabbed wh he 640x480 resoluon wh 4-b rue colors. Fg. shows one sample nghme raffc scene aen from he vson sysem. In hs sample scene, here are movng cars and moorbes on he road, and he salen feaures are her vehcle lghs. In addon o he vehcle lghs of he vehcles, some lamps, raffc lghs and sgns are also he vsble llumnan appeared n he mage sequences of he nghme raffc scenes. Fg.. An example of nghme raffc scene Hence, he frs as s o exrac hese brgh obecs from he road scene mage o faclae furher rule-based analyss. To save he compuaon cos on exracng brgh obecs, we frsly exraced he grayscale mage,.e. he Y-channel, of he grabbed mage by performng a RGB o Y ransformaon. For exracng hese brgh obecs from a gven ransformed graynensy mage, pxels of brgh obecs mus be separaed from oher obec pxels of dfferen llumnaons. Thus, an effecve mullevel hresholdng echnque s needed for auomacally deermnng he approprae number of hresholds for segmenng brgh obec regons from he raffc-scene mage. For hs purpose, we have already proposed an auomac mullevel hresholdng echnque for mage segmenaon []. Ths echnque can auomacally decompose a grabbed road-scene mage no a se of homogeneous hresholded mages. As shown n Fg., brgh obecs of neres can be suably exraced from he brghes one of he resulan hresholded mages. Fg.. Brgh obec plane exraced from Fg. afer performng he brgh obec segmenaon process III. SPATIAL CLUSTERING PROCESS To oban poenal vehcle lgh componens from he deecon zone n he brgh obec plane, a connecedcomponen exracon process [3] s performed o label and locae he conneced-componens of he brgh obecs. By exracng he conneced- componens, meanngful feaures of he locaon, dmenson, and pxel dsrbuon assocaed wh each conneced-componen are obaned. The locaon and dmenson of a conneced-componen can be represened by he boundng box whch encloses. Snce here are varous non-vehcle llumnan lgh componens coexsed wh acual vehcle lghs, such as raffc lamps, road sgns, road reflecor plaes, refleced beams, and some oher llumnan obecs, a spaal classfcaon process s appled on hese brgh componens o prelmnarly deec poenal vehcle lghs and fler ou non-vehcle componens. Then hese deeced poenal vehcle lghs are processed by he followng vehcle lgh racng and denfcaon process o oban he acual movng vehcles. Frs, he defnons used n he proecon-based spaal classfcaon process are descrbed as follows, ). C denoes one ceran brgh conneced-componen o be processed. ). CG denoes a group of brgh componens, CG = {C, =0,,,,p}, he oal number of conneced-componens conaned n CG s denoed as Ncc ( CG ). 3). The locaon of he boundng boxes of a ceran componen C employed n he spaal cluserng process are her op, boom, lef and rgh coordnaes, and hey are denoed as C ( ), bc ( ), lc ( ), and rc ( ), respecvely. 4). The wdh and hegh of a brgh componen C are denoed as WC ( ) and H ( C ), respecvely. 5). The horzonal dsance D h and vercal dsance D v beween wo brgh componens are defned as, Dh( C, C) = max l( C), l( C) mn r( C), r( C) () Dv( C, C) = max ( C), ( C) mn b( C), b( C) () If he wo brgh componens are overlappng n he horzonal or vercal drecon, hen he value of he Dh( C, C ) or Dv( C, C ) wll be a negave value. 6). Hence he measure of overlappng beween horzonal and vercal proecons of he wo brgh componens can be respecvely compued as, 3453

3 P( C, C ) = D ( C, C ) mn H( C ), H( C ) (3) v v P( C, C ) = D ( C, C ) mn H( C ), H( C ) (4) v v (0,0) 3 4 Lane Wdh f3(y )- f(y ) Fg. 3. The llusraon of approxmaed lane wdhs on he mage coordnae An llusrave mage coordnae sysem for vehcle deecon s shown n Fg. 3. In he mage coordnae sysem, he vehcles whch are locaed a a dsan place on he road wll appear n he hgher place and become progressvely smaller, and wll converge no a vanshng pon. Therefore, he drvng lanes sreched from he vanshng pon can be modelzed by a se of lne equaons by, yc f ( y),,... K, (5) m where m and c are he slope and nercep of he -h drvng lane, respecvely. Then he approxmae lane wdh assocaed wh a brgh componen C a a dsance on he mage coordnae, denoed by LW( C ), can be obaned by, LW ( C) f ( CY( C)) f( CY( C)), (6) where CY( C ) represens he vercal poson of he componen C on he mage coordnae, and s defned by CY( C) ( C) b( C). Based on he above-menoned defnons of brgh componens, a classfcaon procedure can be appled on hese obaned brgh componens o prelmnarly deermne poenal vehcle lgh componens and fler ou mos non-vehcle llumnan lgh componens. For hs purpose, a componen C s deermned as a poenal vehcle lgh componen f sasfy he followng condons, ). Sne a vehcle lgh mosly appear n nearly crcular shape, he enclosng boundng box of a poenal vehcle lgh componen should form a square shape,.e. he sze-rao feaure of C mus sasfy he followng condon, TH RL W ( C ) H ( C ) TH RH, (7) where he hresholds TH RL and TH RH for he sze-rao condon are seleced as 0.8 and., respecvely, o suably deermne he crcular-shaped appearance of a poenal vehcle lgh. ). Besdes, a vehcle lgh obec should have a reasonable area as compared o he one of he lane, hus he area feaure of C mus sasfy he followng condon, TH AL < A( C ) < TH AH (8) where he hresholds TH AL and TH AH for he area condon are deermned as TH AL = ( LW ( C ) 8), and TH AH = ( LW ( C ) 4), respecvely, o adapvely reflec he reasonable area characerscs of a poenal vehcle lgh. Afer he prelmnary classfcaon procedure of brgh componens beng performed, alhough mos non-vehcle llumnan lgh componens are flered, here are sll some vehcle-lgh-le componens, such as refleced beams on he road ground. Therefore, a mergng process s hen appled o assocae nearby poenal vehcle lgh componens whch are belonged o he compound vehcle lghs of he same car no lgh groups, and furher fler ou non-vehcle-lgh componens wh ale feaures. Accordngly, f wo neghborng brgh componens C and C sasfy he followng condons, hey are deermned as a homogeneous poenal vehcle lgh se and are merged and clusered as he poenal vehcle lgh se: ). They are horzonally close o each oher,.e.: Dh( C, C) mn WC ( ), WC ( ), (9) ). They are also vercally close o each oher,.e.: Dv( C, C).0 (mn H( C), H( C) ), (0) 3). The wo vercally overlappng brgh obecs havng hgh horzonal proecon profles should be grouped he same group CG: Ph( C, C) > Thp () where he hreshold T hp s chosen as 0.6, o respec he vercal algnmen characerscs of compound vehcle lghs. These represen poenal componens of vehcles, as nomnaed as P n he followng process. Noably, n he curren sage, he vehcle lgh ses on he wo sdes of he vehcle body are no merged no parng groups. Ths s because ha here are vehcles wh parng lgh ses and moorbes wh sngle lgh ses exsng n mos nghme road scenes, and s no easy o deermne wheher nearng vehcle lghs ses are parng lgh ses belonged o he same vehcle whou moon nformaon n he subsequen frames. Thus, a vehcle lgh racng and denfcaon process descrbed n he followng secon wll be appled on hese poenal vehcle lgh ses o denfy he acual movng vehcles and moorbes. IV. TRACKING AND IDENTIFICATION OF POTENTIAL VEHICLES AND MOTORBIKES Our proposed vehcle racng and denfcaon process ncludes hree phases. Frs, he phase of poenal vehcle componen racng process assocaes he moon relaon of vehcle componens n succeedng frames by analyzng her spaal and emporal feaures. Then he phase of moon-based groupng process s appled on he raced vehcle componens o form whole movng vehcles. Accordngly, hese deermned movng vehcles are hen raced by he vehcle racng phase. Nex, he phase of vehcle recognon process denfes and classfes he ypes of he raced vehcles. 3454

4 A. Tracng Process of Poenal Vehcle Componens When a poenal vehcle componen s frsly deeced n he feld of vew n fron of he hos car, a racer wll be creaed o assocae hs poenal vehcle componen wh hose n subsequen frames by applyng her spaal-emporal feaures. The feaures used n he racng process are descrbed and defned as follows: ). P denoes he h poenal vehcle componen appearng n he deecon zone n frame ; and he locaon of P employed n he racng process s represened by s cenral poson, and can be expressed by, l( P ) + r( P ) ( P ) + b( P ) P =, () ). The racer TP s used o represen he raecory of P whch has been raced n sequenal frames o, and s defned as: TP = P, P,..., P (3) 3). The overlappng score of he wo poenal vehcle componens P and P, deeced a wo dfferen me and, can be compued by usng her area of nersecon, A( P P ) So( P, P ) = (4) Max A( P ), A( P ) ( ) In each recurson of he racng process for a newly ncomng frame, he poenal vehcle componens appearng n he ncomng frame, denoed by P = { P =,..., }, wll be analyzed and assocaed wh he se of poenal vehcle componens whch have already been raced n he prevous TP = TP =,...,, and hen frame -, denoed by { } he se of he raced poenal vehcles TP wll be accordngly updaed accordng o he followng process. Durng he racng process, a poenal vehcle componen mgh be n one of hree possble racng saes. The componen racng process apples dfferen relevan operaons accordng o he gven saes of each raced poenal vehcle componen n each frame. The racng saes and assocaed operaons for he raced poenal vehcle componens are as follows: ). Updae: When a poenal vehcle componen P P n he curren frame can mach a raced poenal vehcle componen TP TP f he followng machng condon s sasfed, hen he racer updaes he se of he raced poenal vehcle componens TP by assocang P wh he racer TP. The machng condon s deermned as, So( P, TP ) > 0.5, (5) ). Appear: If a newly comng poenal vehcle componen P P canno mach any TP TP a he prevous me, hen a new racer for hs poenal vehcle componen s creaed and appended o he updaed se TP. 3). Dsappear: A exsng racer of poenal vehcle componen TP TP canno be mached by any newly comng poenal vehcle componens P P. A raced poenal vehcle componen may somemes be emporarly shelered or occluded n some frames, and wll soon re-appear n subsequen frames, hus, o preven such a poenal vehcle componen from beng regarded as a newly appeared poenal vehcle, s racer wll be reaned n he subsequen fve frames. If a racer of poenal vehcle componen TP canno be mached by any poenal vehcles P P for more han fve succeedng frames, hen hs poenal vehcle componen wll be udged o have dsappeared and s racer wll be removed from he racer se TP n he followng frames. Fg. 4. Illusraon of moon-based groupng process B. Moon-Based Groupng of Vehcle Componens Havng he racs of poenal vehcle componens, he moon-based groupng process wll hen be appled o group ogeher poenal vehcle componens whch are belonged o he same vehcles. For hs purpose, poenal vehcle componens whch have rgdly smlar moon n he successve frames wll be grouped no a sngle vehcle. Ths concep s llusraed n Fg. 4. Accordngly, parng racs of nearby poenal vehcle componens TP and TP are deermned ha hey are belonged o he same vehcle f hey have ep coherenly movng and reveal homogeneous feaures for a perod of me, and can be deermned by he followng coheren moon condons, ). They are conssenly movng close wh each oher,.e.: - - LW ( TP ) LW ( TP ) Dh( TP, TP ), (6) - - (mn HTP ( ), HTP ( ) ) and Dv( TP, TP ) where 0,, n, n s deermned o be 0, o appropraely reflec he suffcen susaned me of her common moon nformaon. ). They have smlar heghs for a span of me,.e.: 3455

5 τ τ H ( TP ) H ( TP ) > T (7) S L h where TP τ s he one wh he smaller hegh among he S wo poenal vehcle componens TP and TP a he me, whle TP τ L s he larger one; and T h s chosen o be 0.6 o reasonably reveal he algnmen feaure of parng vehcle lghs. If he racs TP and TP mee he above-menoned coheren moon condons, hen hey are merged no he same componen group rac of a poenal vehcle, denoed by TG. Afer performng he moon-based groupng process on he vehcle componen racs, hen a se of componen group racs, denoed by TG = { TV =,..., }, whch conss of wo or more vehcle componens, can be obaned for he subsequen racng process. C. Tracng Process of Vehcle Componen Groups Durng a poenal vehcles represened by a componen group beng raced across he mage, some possble occluson problems caused by he segmenaon process and he moonbased groupng process may occur. Therefore, havng he poenal vehcle racs of componen groups TG TG obaned by he moon-based groupng process, he componen group racng process wll hen be appled o accordngly updae he poson, moon and dmensons of each poenal vehcle, and progressvely refne he deecon resuls of he poenal vehcles by usng spaal-emporal nformaon n sequenal frames. In hs subsecon, we presen a racng process for componen groups of poenal vehcles o handle he above-menoned occluson problems. Frs, for each raced componen group of a poenal vehcle ll he prevous frame TG TG, s possble locaon n he curren frame wll be prelmnarly esmaed by an adapve search wndow based on s pas moon nformaon. To rapdly deermne he search wndow of a raced vehcle componen group, s moon vecor s frsly compued as, x = CX( TG ) CX( TG ) (8) y = CY( TG ) CY( TG ), where CX( TG ) and CY( TG ) respecvely represens he horzonal and vercal poson of raced componen group TG on he mage coordnae, and are defned by CX( TG) l( TG) r( TG), and CY( TG) ( TG) b( TG), respecvely. Then a dsplacemen facor ( w, w ) whch reflecs he poenal poson of he poenal vehcle n he curren frame can be compued as, x w = + x, y (9) y w = + x, y The cener of he search wndow of a raced poenal vehcle n he curren frame can be deermned as ( w C TG ), w C ( TG ), and s wdh and hegh can be ( X Y ) defned as.5 WTG ( ), and 3 HTG ( ), respecvely. The possble posons of raced poenal componens TP whch are mached wh a raced poenal componen group TG n he curren frame can be more rapdly and correcly obaned n he search wndow. For a raced componen group TG found n he search wndow, may be n four possble saes assocaed wh s own componen racs TP,, TP + n. Ths poenal vehcle racng process wll conduc dfferen operaons accordng o he curren sae of TG as: ). Updae: all of he grouped componen racs TP,, TP + n owned by a raced componen group TG n he prevous frame can sll exacly and respecvely mach a se of he vehcle componen racs TP,, TP n he n curren frame whn he search wndow,.e. hey all sasfy he followng machng condon, So( TP, TG ) > 0.5 (0) Then he vehcle racer us updaes ha he componen group TG of a poenal vehcle o be comprsed of he renewed group of TP,, TP. n ). Sheler/Absorb: he grouped componen racs TP,, TP + n owned by TG n he prevous frame now have lesser number of componen racs TP,, TP m (where m n) o be found n he curren frame whn he search wndow. The machng condon (Eq. (0)) of he componen group TG wh TP,, TP wll be m respecvely checed, hen he ones whch are sasfed wh he machng condon wll sll be assocaed wh he renewed TG. For he racs of unexpecedly dsappeared or absorbed componens whch are mssed from TG, hey wll be sll reaned n he TG unl hey are regarded as dsappeared componens and removed by he poenal vehcle componen racng process. 3). Exend/Spl: he grouped componen racs TP,, TP + n owned by TG n he prevous frame now are exend or spl no more number of componen racs TP,, TP (where m n) n he curren frame whn he m search wndow. The machng condon (Eq. (0)) of TG wh TP,, TP wll be respecvely checed, hen he m ones whch are concde wh TG wll sll be assocaed wh he renewed TG. For he racs of newly appeared or spl componens whch are no mached wh TG, he moon-based groupng process (Eq. (6), and (7)) wll be appled on hese non-mached componen racs o chec f hey have coheren moon propery wh TG, and hen he ones havng coheren moon wll be assgned no he 3456

6 renewed TG, and he ohers wll be deached o be orphan componen racs. 4). Ex: a raced poenal componen group TG has moved across he boundary of he deecon regon, and now all s own componen racs are deermned o be dsappeared by he poenal vehcle componen racng process. D. Vehcle Idenfcaon and Classfcaon from Tracng Process Durng he racng process of he poenal vehcles, a rulebased vehcle verfcaon and classfcaon process s hen appled on each of he poenal componens and componen groups of poenal vehcles havng been raced for a me of more han 0 frames, o deermne and classfy wheher comprses a car, a moorcycle, or oher on-road llumnaed obecs. Car denfcaon Frs, o denfy movng cars n frame sequence, we can reasonably assume ha a group of lghng componens may have a hgher possbly o be a car. Therefore, for a raced componen groups TG whch has been conssenly raced by he componen group racng process for a span of more han 0 frames afer beng creaed by he moon-based groupng process, hen TG can be nomnaed as a canddae of a movng car. Accordngly, f TG conans a se of acual vehcle lghs ha reveal an acual car, hen TG mosly sasfy he followng dscrmnang rules of sascal feaures: ). Snce a movng car can be approxmaely modeled as a recangular pach, he enclosng boundng box of a poenal car should form a horzonal recangular shape,.e. he sze-rao feaure of he enclosng boundng box of TG mus sasfy he followng condon, ( ) H( TG ) τr W TG τr () where he hreshold τ r and τ r on he sze-rao condon are seleced as.0 and 0.0 o suably denfy he recangular-shaped appearance of pared vehcle lghs. ). Moreover, he number of he lghng componens of TG should also be symmercal and well-algned, and hus he number of hese componens should be n reasonable proporon o he sze of he sze-rao feaure of s enclosng boundng box, hus, he followng algnmen condon should be sasfed, WTP ( ) WTP ( ) τa Ncc( TP ) τa () HTP ( ) HTP ( ) where he hresholds τ a and τ a are deermned as 0.4 and.0, respecvely, accordng o our analyss of ypcal vsual characerscs of mos movng cars appeared n he raffc scenes durng nghme drvng. mosly appeared as a sngle, and nearly square-shaped or vercal recangular-shaped lghng componen. Thus, for a sngle raced componen TP whch has no been assocaed o any componen groups and been conssenly and alone raced by he vehcle componen racng process for a span of more han 5 frames, hen TP can be deermned as a canddae of a movng moorbe. Therefore, f a sngle raced componen TP s acually a moorbe, hen he sze-rao feaure of s enclosng boundng box should reflec a square or vercal recangular shape, and should sasfy he followng dscrmnang rule: ( ) H( TP ) τm W TP τm (3) where he hreshold τ m and τ m on he sze-rao condon are seleced as 0.6 and. o suably denfy he shape appearance characersc of he moorbes. Accordngly, a raced componen group or sngle poenal componen of a poenal vehcle wll be denfed and classfed as an acual car or a moorbe accordng o he above-menoned vehcle classfcaon rules. V. EXPERIMENTAL RESULTS The proposed sysem s mplemened on a Penum-4.4 GHz plaform whch s se up on our elevaed plaform of he hghway and urban roads. The frame rae of he vson sysem s 30 frames per second and he sze of each frame of grabbed mage sequences s 640 pxels by 480 pxels per frame. TABLE. Expermenal daa of our proposed approach on an urban road Lane Deeced Vehcles Acual Vehcles Lane Lane Lane Toal No. Cars Toal No. Moorbes Deecon Rae of Cars 97.58% Deecon Rae of Moorbes 98.48% Tme span of he vdeo 50 mnues The proposed sysem has been esed on several vdeos of real nghme raffc scenes n varous condons. Fgures 5 6 exhb he mos represenave ones of he expermenal samples on performance evaluaon. As shown n Fg. 5, he movng cars and moorbes n an urban road are correcly deeced and raced by locang s vehcle lghs, alhough some oher non-vehcle llumnaed obecs also coexs wh he vehcle n hs scene. TABLE depcs he quanave resuls of he proposed approach on vehcle deecon and racng n urban road. Moorbe denfcaon For he purpose of denfyng he moorbes, we can adop he fac ha a moorbe n he nghme raffc scenes s 3457

7 effecvely sasfy he demand of real-me processng a more han 30 frames per second. As can be seen from he expermenal resuls, he proposed sysem demonsraes ha can provde fas, real-me, and effecve nghme vehcle deecon and denfcaon performance for raffc survellance. Fg. 5. Resuls of vehcle deecon and racng on he nghme urban raffc scene TABLE. Expermenal daa of our proposed approach on he hghway scene Lane Deeced Vehcles Acual Vehcles Lane Lane Lane Toal No. Cars Deecon Rae of Cars 97.73% Tme span of he vdeo 50 mnues Fg. 6. Resuls of vehcle deecon and racng on he nghme hghway raffc scene As shown n Fg. 6, anoher sample of raffc scene of nghme hghway s llusraed. The vehcle lghs of mulple movng vehcles are close o each oher n hs raffc scene, and he proposed mehod sll successfully deec and rac almos all vehcles by locang her vehcle lgh pars. TABLE shows he quanave resuls of he proposed approach on vehcle deecon on he nghme hghway. For an npu vdeo sequence wh 640 x 480 pxels per frame, he proposed sysem aes an average of 6 mllseconds processng me per frame. Ths frugal compuaon cos ensures ha he proposed sysem can ACKNOWLEDGEMENTS Ths wor was suppored by he Naonal Scence Councl of R.O.C. under Conrac No.: NSC E REFERENCES [] S. Kamo, Y. Masusha, K. Ieuch, M. Saauch, Traffc monorng and accden deecon a nersecons, IEEE Trans. Inell. Transpor. Sys., vol., no., pp.08-8, 000. [] A. H. S. La and N. H. C. Yung, Vehcle-ype denfcaon hrough auomaed vrual loop assgnmen and bloc-based drecon-based moon esmaon, IEEE Trans. Inell. Transpor. Sys., vol., no., pp.86-97, 000. [3] R. Cucchara, M. Pccard, and P. Mello, Image analyss and rule-based reasonng for a raffc monorng sysem, IEEE Trans. Inell. Transpor. Sys., vol., no., pp.9-30, Jun [4] M.-C. Huang and S.-H. Yen, A real-me and color-based compuer vson for raffc monorng sysem, IEEE In'l Conf. Mulmeda Expo, pp.9-, May [5] J. Kong, Y. Zheng, Y. Lu, B. Zhang, A novel bacground exracon and updang algorhm for vehcle deecon and racng, IEEE In'l Conf. Fuzzy Sys. nowled. Dscov., 007. [6] S. Gupe, O. Masoud, R. F. K. Marn, N. P. Papanolopoulos, IEEE Trans. Inell. Transpor. Sys., vol. 3, pp , 00. [7] B.-F. Wu, S.-P. Ln, Y.-H. Chen, A Real-me mulple-vehcle deecon and racng sysem wh pror occluson deecon and resoluon, IEEE In l Symp. Sg. Process. Info. Tech., pp.3-36, Dec [8] D. Beymer, P. McLauchlan, B. Cofman, and J. Mal, A realme compuer vson sysem for measurng raffc parameers, IEEE Conf. Compu. Vs. Pa. Recog., pp , Jun.997. [9] J. Zhou, D. Gao, and D. Zhang, Movng vehcle deecon for auomac raffc monorng, IEEE Trans. Veh. Tech., vol. 56, no., pp.5-59, Jan [0] K. Huang, L. Wang, T. Tan, and S. Mayban, A real-me obec deecng and racng sysem for oudoor ngh survellance, Paern Recogn., vol. 4, pp , 008. [] N. Osu, A hreshold selecon mehod from gray-level hsograms, IEEE Trans. Sys., Man, Cybern., vol. SMC-9, pp. 6-66, 979. [] B.-F. Wu, Y.-L. Chen, and C.-C. Chu, A dscrmnan analyss based recursve auomac hresholdng approach for mage segmenaon, IEICE Trans. Info. Sysems, vol. E88-D, no.7, pp.76-73, 005. [3] K. Suzu, I. Horba, and N. Suge, Lnear-me connecedcomponen labelng based on sequenal local operaons, Compuer Vson & Image Undersand., vol. 89, pp. -3,