VEHICLE DETECTION BY USING REAR PARTS AND TRACKING SYSTEM

IJRET: Internatonal Journal of Research n Engneerng and Technology eissn: 2319-1163 pissn: 2321-7308 VEHICLE DETECTION BY USING REAR PARTS AND TRACKING SYSTEM Yogn Ashokrao Kanhegaonkar 1, Jagtap Rupal Ramesh 2 1 M.E Student,Electroncs and Telecommuncaton, Annasaheb Dange College of Engneerng, Ashta, Maharashtra, Inda 2 Asst. Prof., Electroncs and Telecommuncaton, Annasaheb Dange College of Engneerng, Ashta, Maharashtra, Inda Abstract Vson of Indan government; of makng 100 smart ctes, attracts our attenton to ntellgent transport system. Traffc flow analyss s a part of ntellgent transport system. It manly contans three parts: vehcle detecton, classfcaton and vehcle trackng par t. Recently, there are dfferent detecton and trackng methods lke computer vson based, magnetc frequency wave based etc. Wth the rapd development of computer vson technques, vsual detecton has become ncreasngly popular n the transportaton feld. In urban traffc vdeo montorng systems, traffc congeston s a common scene that causes vehcle occluson and s a challenge for current vehcle detecton methods.in practcal traffc scenaros, occluson between vehcles often occurs; therefore, t s unreasonable to treat the vehcle as a whole. To overcome ths problem we can use part based detecton model. In our system the vehcle s treated as an object composed of multple salent parts, ncludng the lcense plate and rear lamps. These parts are localzed usng ther dstnctve color, texture, and regon feature. Furthermore, the detected parts are treated as graph nodes to construct a probablstc graph usng a Markov random feld model. After that, the margnal posteror of each part s nferred usng loopy belef propagaton to get fnal vehcle detecton. Fnally, the vehcles trajectores are estmated usng a Kalman flter and a trackng-based detecton technque s realzed. Ths method we can use n daytme as well as nght tme and n any bad weather condton. Keywords vehcle detecton, kalman flter, Markov model, trackng, rear lamps --------------------------------------------------------------------***------------------------------------------------------------------ 1. INTRODUCTION 1.1 Traffc Survellance The urban as well as the natonal road networks have ncreased n large number for the past thrty years and hence, ths development n road networks have necesstated some means to montor and handle the road traffc. The tradtonal methods for measurng the traffc nclude sensors or EM mcrowave detectors and nductve loops. But, they own severe drawbacks lke, traffc dsrupton at the tme of nstallaton and repar, huge sze, costler nstallaton and lack of ablty to dentfy the vehcles that are not fast or not permanently ceased. In contrast, the systems that work dependng on vdeo have smple nstallaton steps and utlze the already avalable traffc survellance nfrastructure. Addtonally, enhancements can be made wthout much dffculty and they are so flexble n redesgnng the system as well as the system s functonalty through just modfyng the algorthms that operate the system. Wth these systems, t s possble to obtan the measurements that are related to the vehcle such as, speed of the vehcle, calculatng the vehcle s count, categorzng the vehcle and fndng out the events occurrng n the traffc ( lke heavy congeston, accdents and smlar other events). Plenty of systems, whch make use of approaches that come under the feld of vdeo and mage processng, have been developed for detectng vehcles and the other objects. 1.2 Vdeo Processng on Traffc Survellance One of the hot research themes n computer vson that draws ncreased attenton s the traffc survellance system. Wth ths system, the vehcles from an mage seres can be dentfed and tracked effcently. Further, t gves clear detals about the behavor of the object or the vehcle n the mage seres and t makes montorng as well as the management of traffc easer because t s a better replacement for the tradtonal method nvolvng human operators, who go around montorng cameras manually. Usng computer vson system, the unauthorzed actvty that has taken place just before or the mstrustful event that has been produced at an earler tme can be montored and thus, the human operators are supported n makng thorough nvestgaton of the happenng. 1.3 Types of Vdeo Survellance System The varous knds of vdeo survellance system are as follows: Manual traffc survellance system Sem-automatc traffc survellance system Fully-automatc traffc survellance system Volume: 04 Issue: 08 August-2015, Avalable @ http://www.jret.org 162

IJRET: Internatonal Journal of Research n Engneerng and Technology eissn: 2319-1163 pissn: 2321-7308 1.3.1 Manual Traffc Survellance System Ths system completely nvolves a human operator for traffc montorng. The human operator only observes the vsual nformaton, whch s captured from varous cameras. The operator s subjected to ncreased level of dffculty, snce he has to note all the multple screens. In addton, the operator s prevented from showng more attenton towards adverse happenngs. Ths knd of systems can never prove effectve n places that are massve and actve because the cameras under usage would rse n number that they go beyond the human expert s ablty. These systems are more prevalent n almost all the parts of the world. 1.3.2 Sem-Automatc Traffc Survellance System In these types of systems, the human operator as well as the computer vson s nvolved. The computer vson algorthm helps n trackng the object, whle the human operator performs other functons lke, personal dentfcaton, categorzaton and actvty recognton. It employs vdeo processng of lower level and the human operator s mostly nvolved n montorng and controllng the traffc. 1.3.3 Fully-Automatc Traffc Survellance System Here, the computer vson performs all the tasks wthout the support from the human operator. These systems are more ntellgent that they are capable of trackng, classfyng and recognzng the object under nterest[c]. Moreover, n case of mstrustful events, these systems would ntellgently dentfy those events and performs the object s actvty recognton procedure. The vsual survellance systems come nto play n stuatons of urban envronment such as, montorng of congeston n the road, vehcle nteracton montorng and dentfyng the vehcles that does not follow the rules [23]. 1.4 Need of Vdeo Survellance System The number of severe accdents can be cut down wth the vdeo survellance system and henceforth, so many lves could be safeguarded. A most dedcated applcaton of vdeo analytcs that has undergone thorough research s the ANPR. In the toll statons of freeways, devoted lanes contanng cameras are avalable and therefore, the regstered users can pass the toll staton n a slower manner wthout ceasng [35]. On the other hand, the nner cty congeston charge systems (for nstance, Stockholm, Sweden; London, U.K., and Sngapore) show decreased level of ntrusveness and functon on the traffc that pass n a normal way. In almost all the traffc survellance systems, the desred traffc parameters that nclude vehcle dentfcaton, trackng and classfcaton can be assessed usng three mportant phases. Majorty of the methods [25], [26], [27], [28], [29], [30], [31] that are employed n vehcle detecton consder the camera to be statc and then, the requred vehcles can be dentfed usng the mage dfferencng method. In computer vson, trackng can be thought as a serous problem. Survellance applcatons are drawng more attenton nowadays. Trackng n computer vson s manly requred to dentfy and spot a prototype from a seres of sequental frames. Trackng s ncreasngly used n applcatons lke securty, survellance and automatc processes as well as vdeo processng. Later, varyng trackng schemes are to be desgned for trackng every sngle vehcle. As the next step, the features of dfferent vehcles such as length, shape, texture, wdth, lcense number and smlar other features are extracted to allow the categorzaton of vehcles. In all the vsual survellance systems, the frst step s the detecton of object n moton from the vdeo streams [32]. Traffc survellance systems can effectvely support plenty of applcatons that nclude congeston analyss, mltary securty, commercal and publc securty, crowd flux statstcs, vsual survellance and person dentfcaton, detecton of anomalous behavor and many more. 1.5 On-Road Vehcle Detecton and Trackng In all natons, accdent avodance and guaranteeng road safety s an mportant ssue. Thus, several natonal as well as nternatonal researches pertanng to ths ssue has been developed n recent years [16]. On-road vehcle detecton, wheren the vehcle s dentfed from the mages that were captured durng the moton of the vehcle on road, s one such research topc [21]. The vehcle detecton procedure supports the ntellgent transport system applcatons that nclude drver-assstance systems, self-guded vehcles and automatc parkng systems, n addton to ensurng road safety [19]. In prototype vehcles, actve sensors lke, lasers can be utlzed. The dsadvantages of usng such sensors are hgh expense, nterference wth the other electronc devces and decreased resoluton level. Therefore, the utlzaton of passve sensors lke, the camera was more benefcal n the subsequent years. Processng vsual data that s acqured from the camera seems to be more sgnfcant [18]. Despte the fact that the camera s powerful, processng of poor vsual nformaton usng passve sensors n the vehcle detecton procedure may end n poor relablty levels [17]. It s at ths juncture, the computer vson algorthms do an essental task. 1.6 Practcal Challenges Some of the serous ssues that are faced durng the vehcle detecton and trackng process nclude the speed, color and sze that are owned by dfferent vehcles, the constant varatons n the landscape at the sdes of the road, llumnaton changes n accordance wth day and nght, weather changes and other parameters that are assocated wth the vehcle [19] [22]. These ssues have been made complex further due to very fast urbanzaton, traffc and the drecton towards whch the vehcle moves. Therefore, presently, an effcent as well as automatc vehcle detecton and trackng system s under demand [20]. Our am s ntroduce a robust vehcle detecton and trackng system to nvestgate the potental features for llustratng the multple components of the vehcle facltate the vehcle detecton and trackng process mprove the performance of vehcle detecton and trackng results Volume: 04 Issue: 08 August-2015, Avalable @ http://www.jret.org 163

IJRET: Internatonal Journal of Research n Engneerng and Technology eissn: 2319-1163 pissn: 2321-7308 And expermentally nvestgate the vehcle detecton and trackng system and to demonstrate ts performance Ths paper makes use of the assocaton that exsts among the locaton of the rear lamps and the lcense plate to accomplsh the vehcle detecton and trackng approach. The Kalman flter has been employed to get the ntal predcton of the object n moton. Yet, the vehcle detecton and trackng that s acheved at last s performed usng the markov model. Ths markov model produces the output n accordance wth the results of Kalman flter and the relatonshp that s present between the postons of the rear lamps and the lcense plates. The objectves of the project are as follows: To develop a robust color space model to segregate rear lamps and rear lcense plate from the movng vehcle To detect and estmate the vehcle based on the rear lamps and lcense plate wth remarkable precson To develop an user nterface to facltate the executon of the methodology To demonstrate the performance of the methodology To ensure the practcal vablty of the methodology ensure that equatons ft n the gven column wdth I RC I R I RC max I R I, I 2 I I :0 N 1 I G G B (1) I B where,,, and refer to the mage n rear lamp color space, R band, G band and B band, respectvely. represents norm functon and N refers to the total number of frames or mages n the sequence. In order to mprove the precson of the rear lamp detecton precson, a post processng step s ncluded. In the post I processng step, frstly the contrast of RC s mproved followed by bnarzng the contrast enhanced mage. The bnarzed mage s morphologcally eroded and hence the rear lamps are obtaned n the mages as whte pxels, whereas the remanng foreground and background objects appear as black pxels. G B th 2. PROPOSED METHODOLOGY The proposed vehcle detecton and trackng system s comprsed of four stages, namely, vehcle rear lamp detecton, rear lcense plate detecton, estmatng moton object and detectng vehcle. The archtecture dagram of the proposed vehcle detecton and trackng algorthm s llustrated n Fgure 1. Accordng to Fgure 1, the nput vdeo s subjected to mage sequencng n whch the vdeo s defned as sequence of mages n standard sze. These mage sequences are subjected to three stages, namely rear lamp detecton, rear lcense plate detecton and moton estmaton usng kalman flterng method. A markov model s constructed based on the nformaton obtaned from rear lamp detecton and rear lcense plate detecton. Ths markov model s subjected to detect and track the vehcle based on the moton estmaton results. 2.1 Rear Lamps Detecton The rear lamp detecton process gets mage sequence and undergoes three basc operatons such as color space converson, post processng and detectng. The mage sequences are acqured n RGB color space. However, rear lamps cannot be vsualzed or detected n ths color space. Hence, the sequences are converted from RGB color space to a dfferent color space n whch the rear lamps can be detected precsely. We have adopted the color space converson model proposed n [1], whch are as follows. Please note that math equatons mght need to be reformatted from the orgnal submsson for page layout reasons. Ths ncludes the possblty that some n-lne equatons wll be made dsplay equatons to create better flow n a paragraph. If dsplay equatons do not ft n the two-column format, they wll also be reformatted. Authors are strongly encouraged to Fg 1 Archtecture dagram of the proposed methodology 2.1 Rear Lcense Plate Detecton The processes ncluded n detectng the rear lcense plate of the vehcle are smlar to the rear lamp detecton processes, except the color space converson model. Accordng to [1], the color space converson model that supports rear lcense plate of the vehcle s gven as follows: mn( I Volume: 04 Issue: 08 August-2015, Avalable @ http://www.jret.org 164 I LP I B R, I G ) (2)

IJRET: Internatonal Journal of Research n Engneerng and Technology eissn: 2319-1163 pissn: 2321-7308 I LP th where, s the mage n lcense plate color space. Smlar to rear lamp detecton process, the mages n lcense late color space model are subjected to contrast enhancement, bnarzaton and morphologcal eroson to detect the rear lcense plate of the vehcle. 2.2 Moton Estmaton usng Kalman Flterng Moton estmaton s the process of determnng moton vectors from a sequence of frames. Explotng kalman flterng process for moton estmaton has been reported n the lterature snce 1990s [2]. It has been referred as one of the promsng technques for effectve moton estmaton and hence numerous varants of ths algorthm have also been reported [3] [4]. Hence, our paper explots kalman flter to estmate the movng object n the gven mage sequence. The estmated movng object s used for detectng rear lamps, lcense plate and also for markov decson. It s known that n the rear lamp and lcense plate detecton processes, rear lamps and lcense plate are n whte pxels and the other foreground and background objects are n black pxels, respectvely. However, t shall not be precse enough to drop all such undesred objects. Based on the kalman flterng output, the msnterpreted rear lamps and lcense plate are dropped. Smlarly, the kalman flterng output s used at the fnal decson makng process also. 2.3 Markov Model for Detectng/Trackng Vehcle The markov model for detectng/ trackng vehcle s constructed based on the relatonshp between the postons of the left and rght rear lamps and the rear lcense plate. Once the probablty of beng the vehcle s ensured by the markov model, the fnal predcton mprovement s taken place usng the predcton results of kalman flter. The markov model s constructed based on fve nterpreted condtons as follows l Condton (): l Condton (): Condton (): left x L x rght x L x left rght Lx Lx (3) (4) (5) left l y L Condton (v): y rght l y L Condton (v): y Based on the aforesad condtons, emsson matrx and the decson model are constructed. 3. RESULTS AND DISCUSSION 3.1 Expermental Results The proposed methodology s smulated n MATLAB and expermental nvestgaton s carred out. For a gven vdeo, the rear lamps and lcense plate are detected followed by moton estmaton usng kalman flter. The mage results obtaned for the three processes are gven n fgures 2, 3 and 4, respectvely. There are multple localzatons of rear lamps and lcense plates. For nstance, left top and rght bottom mages of fgure 2 have more mnor centrods. However, these mprecse centrods wll be neglected, when the condtons gven n Equatons (3) (7) are consdered. Smlar crcumstances occur n fgure 3 also. Fgure 4 llustrates the predcton of movng object by kalman flter. These predcton results are hghly useful for the markov model to detect and track the vehcles. 3.2 Error Metrc The performance of the proposed methodology s nvestgated usng error metrcs. The error s determned as the absolute dfference between the centrod of the actual vehcle and predcted vehcle. There are 56 frames n the subjected nput vdeo and hence we have determned 56 error metrcs for all the frames followed by averagng them. Thus obtaned mean error s around 11.40 (approx.). The box plot and nstantaneous error are llustrated n fgures 5 and 6, respectvely. The box plot n fgure 5 shows that the medan error metrc remans n around 8 (approx.), whereas the mnmum and maxmum error reach to 0 (approx.) and 32 (approx.), respectvely. Few outlers shall go beyond 100 (approx.). More detaled and nstantaneous verson of the metrcs can be vsualzed through fgure 6. (6) (7) (a) (b) (c) (d) (e) Fg.2. Detecton and trackng of rear lamps for an mage sequence. Volume: 04 Issue: 08 August-2015, Avalable @ http://www.jret.org 165

IJRET: Internatonal Journal of Research n Engneerng and Technology eissn: 2319-1163 pissn: 2321-7308 (a) (b) (c) (d) (e) Fg. 3. Detecton and trackng of lcense plate for an mage sequence. Please note that the gven sequence s ntermedate samples and not temporally nterlnked wth each other. (a) (b) (c) (d) (e) Fg. 4. Detecton and trackng of vehcle by kalman flter for an mage sequence. Please note that the gven sequence s ntermedate samples and not temporally nterlnked wth each other. Fg 5 Box plot to descrbe the statstcal relatonshp of the error between the detected and the actual posture of the vehcle Fg 6 Instantaneous error between the detected and the actual posture of the vehcle Fg 7 User nterface of the proposed methodology developed n MATLAB Volume: 04 Issue: 08 August-2015, Avalable @ http://www.jret.org 166

IJRET: Internatonal Journal of Research n Engneerng and Technology eissn: 2319-1163 pissn: 2321-7308 3.3 User Interface A user nterface s developed n MATLAB for workng on the methodology. The developed nterface s llustrated n fgure 7. The nterface has a button, named as Browse vdeo to browse and load nput vdeo, and an executon button, named Track vehcle, to track vehcles n the loaded vdeo. The correspondng vdeos are played just below the buttons, whereas the status of the vdeos (whether t s beng played or stopped) s dsplayed just below the vdeo. The nterface has three Dsplay optons to dsplay the centrods of detected rear lamps and lcense plate as well as ts approxmated boundng regon. Moreover, there s an opton to dsplay the kalman flter output also. The performance metrcs are nstantaneously updated n the Instant error box and the average of the nstant errors are dsplayed n the average error box. The nterface s developed for smple executon of the methodology. It shall be subjected to further enhancements so that the user can access the methodology ntensely. 4. CONCLUSION Ths paper has ntroduced a methodology to detect and track vehcles based on the locaton of rear lamps and lcense plate. Rear lamps and lcense plate of vehcles have been determned n a dedcated color space model to mprove the precson. Kalman flter has been used to estmate the moton object as well as to ensure the performance of the rear lamp detecton process and lcense plate detecton process. The spatal relatonshp between the locatons of the rear lamps and the lcense plate has been used to construct the markov model and hence the vehcle has been detected and tracked throughout the vdeo. The expermental study on the methodology has revealed ts reduced devaton between the actual vehcle and the tracked vehcle. 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