Nighttime Motion Vehicle Detection Based on MILBoost

Transcription

1 Sensors & Transducers 204 by IFSA Publshng, S L Nghtte Moton Vehcle Detecton Based on MILBoost Zhu Shao-Png,, 2 Fan Xao-Png Departent of Inforaton Manageent, Hunan Unversty of Fnance and Econocs, Changsha, 40205, Chna 2 School of Inforaton Scence and Engneerng, Central South Unversty, Changsha 40075, Chna Tel: E-al: zhushaopng_cz@63co Receved: March 204 /Accepted: 30 Aprl 204 /Publshed: 3 May 204 Abstract: Ths paper propose an effectve approach for detectng and tracng ovng vehcles n nghtte traffc scenes Vehcles were detected autoatcally fro vdeo sequences at nghtte by constructng the MILBoost odel At frst, we extract SIFT feature usng SIFT feature extracton algorth, whch s used to characterze ovng vehcles n nghtte Then MILBoost odel s used for the on-road detecton of vehcles at nghtte In order to prove the detecton accuracy, the class label nforaton was used for the learnng of the MILBoost odel Fnal experents were perfored and evaluate the proposed ethod at nghtte under urban traffc condton, the experent results show that the average detecton accuracy s over 987 %, whch valdates that the proposed vehcle detecton approach s feasble and effectve for the on-road detecton of vehcles at nghtte and dentfcaton n varous nghtte envronents Copyrght 204 IFSA Publshng, S L Keywords: Huan acton, Boostng algorth, Space-te nterest ponts, Bag of Words Introducton In recent years, vson-based vehcle detecton s of great scentfc and practcal portance n any related applcatons, such as self-guded vehcles, drver assstance systes, ntellgent parng systes, or n the easureent of traffc paraeters, whch s le vehcle count, speed and flow Due to the decreasng costs and ncreasng power of coputers, vson-based vehcle detecton technology plays an ncreasngly portant role n traffc ontorng and ntellgent transportaton systes However, the detecton of vehcles based on vdeo at dayte allows drver assstance systes to avod collsons and prove safety, le the well nown adaptve cruse control But at nghtte, any vdeo-based vehcle detecton algorths durng dayte can't be used, because ost state-of-the-art features cannot be easured, whch are le shadows, syetry and others The on-road detecton of vehcles at nghtte s very challengng for developng a robust and effectve syste of vson-based, because of the shadows of vehcles, varable llunaton condtons and varable weather condtons For exaple, the shadows of the ovng vehcles ay easly be regarded as a part of the vehcles n sunlght, whch results n ncorrect segentaton At nght, vehcle headlghts and bad llunaton ay cause any dffcultes for accurate vehcle detecton In ths paper, we propose an effectve approach for detectng and tracng ovng vehcles n nghtte traffc scenes The proposed algorth, ncludng SIFT feature extracton and MILBoost classfer detecton In the extractng feature, features of ovng vehcle at nghtte are extracted by usng SIFT feature extracton algorth, whch s used to 93

2 characterze ovng vehcles n nghtte Then MILBoost odel s used for the on-road detecton of vehcles at nghtte In order to prove the detecton accuracy, the class label nforaton was used for the learnng of the MILBoost odel Fnal experents were perfored and evaluate the proposed ethod at nghtte under urban traffc condton, the experent results show that the proposed vehcle detecton approach s feasble and effectve for the on-road detecton of vehcles at nghtte and dentfcaton n varous nghtte envronents The rest of ths paper s organzed as follows Secton 2 gves a bref survey of soe recent wor on the ovng vehcle detecton After revewng related wor, we descrbe SIFT feature extracton algorth n secton 3 Secton 4 gves detals of MILBoost algorth for the ovng vehcle detecton at nghtte Secton 5 shows experent result, also coparng our approach wth two state-of-the-art ethods, and the conclusons are gven n the fnal secton 2 Related Wor Vehcle based on vson speed easureent (VSM) s one of the ost convenent ethods avalable n ntellgent transportaton systes The on-road detecton of vehcles at nghtte has becoe a hot spot Treendous aount of researches have been carred out n the feld of autoatc vehcle detecton fro vdeo sequence Matthews et al [] proposed two-stage vehcle detecton and recognton algorth by cobnng an age processng regon of nterest (ROI) desgnator to cue a secondary recognton process pleented usng prncpal coponent analyss (PCA) as nput to a Mult- Layered Perceptron (MLP) classfer, whch have been desgned for real-te pleentaton and datafuson wth other nforaton sources Tsa et al [2] proposed a detectng vehcles approach by stll ages n 2007, whch based on color and edge features Ths approach can detect vehcles wthout oton nforaton or slowly ovng vehcles to be effcently detected fro age sequences Vargas et al and Toral et al [3] used bacground subtracton to extract oton nforaton fro vdeo sequences and detected ovng vehcles Wan et al [4] proposed a novel algorth to extract par and trac headlghts usng two thresholds n 20 Zhang et al [5] presented the based-analyss of the lght attenuaton odel usng a reflecton ntensty ap and a suppressed reflecton ap n order to extract the headlghts The accuracy rate of headlght detecton obtaned 952 %, but the vehcle tracng rate was only 882 % n 202 However, these ethods dd not consder vehcles,whch have not lghts, only lght, or all lghts shelded by others, and dd not fully consder the reflectons of the headlghts A lot of wor has been done n detectng the ovng vehcle fro both stll ages and vdeo sequences In ths paper, we concentrated on detectng vehcles wth MILBoost ethod The proposed algorth, ncludng SIFT feature extracton and MILBoost classfer detecton Frst, pxels of the ovng vehcle at nghtte are extracted fro the captured age sequences by usng the SIFT ethod Second, the pxels of the ovng vehcle are grouped and atched to obtan characterstcs of the related coponents The locatons and szes of the related coponents are used for the ovng vehcle parngs A related coponent of the ovng vehcle coposed of an nstance and cae nto the sae bag Fnally, the bags are classfed by MILBoost ethod to detect vehcles Experental results show that our proposed approach can robustly and effectvely detect the ovng vehcles under coplcated nghtte traffc condtons 3 SIFT Feature Extracton Algorth Most of the features used for ovng vehcles detecton, such as color, shadows, edges and oton nforaton, are dffcult or possble to extract n dar or nghtte stuatons Hence, soe feature extracton ethods are nadequate n dar or nghtte traffc condtons However, nghtte traffc condtons are coplcated and chaotc, wth any potental lght sources, such as traffc lghts, street lghts and reflectons fro vehcle headlghts The on-road detecton of vehcles at nghtte s a dffcult proble In age atchng and retreval, age feature extracton s an portant technology Iage features can be dvded nto global features and local features Global features anly descrbe the statstc nforaton of the whole age, and local features reflect the detals of the structure and texture of local areas of the age [6] Local features have a hgher robustness and broader applcaton [7] The studes have shown that n the local features SIFT has the hghest accuracy [8, 9, 0] The ethods based on SIFT have been appled to any varous probles, such as age taggng [], object classfcaton [2], large-scale oble vsual search [3] and duplcated regons detecton [4] Although nghtte traffc condtons are coplcated and chaotc, vehcle body are vsble at nghtte due to traffc lghts, street lghts and reflectons fro vehcle headlghts We utlzed SIFT algorth to extract SIFT feature for the ovng vehcle The process of SIFT feature extracton s as follows: Step : fndng extrees n ult-scale space, whch s expressed as: Dxy (,, σ) = ( Hxy (,, σ) Hxy (,, σ)) I(, xy), = Lxy (,, σ) Lxy (,, σ) where (, ) I x y s the nput age, (,, ) Lxyσ s the age scale space, 94

3 2 2 ( x + y ) 2 H( x, y, σ) = e /2σ, H( x, y, σ ) s the 2 2πσ Gaussan functon of varable denson Step 2: locatng the extrees and fndng the correspondng ey ponts and ther postons and scales; Step 3: assgnng drecton paraeters to each ey pont usng the prncpal drecton of the ey pont gradent n ts neghbor as the characterstc drecton to acheve scale and drecton nvarance for the descrptor, whch s expressed as: Gxy (, ) = ( Lx ( +, y) Lx (, y)) + ( Lxy (, + ) Lxy (, )) 2 2 θ ( x, y) = arctan2( L( x, y+ ) L( x, y )) / ( L( x+, y) L( x, y)) Step 4: generatng ey ponts descrptor Each feature pont of 6 6 neghborhood s dvded nto 6 sub regons, whch the sze of each sub regon s 4 4, calculate the gradent and the gradent hstogra of each subdoan n eght drectons and obtan the densons SIFT feature vector, whch s a total of 28 densons, and noralze the feature vector Although SIFT feature exacton has the hghest accuracy, SIFT feature pont descrptor wth 28 densons are generated wth gradent of the ey pont n ts neghbor Therefore, t has a hgh coputatonal coplexty and serous consupton of the coputng resources [5, 6] To solve ths ssue, we proposed a new approach based on MILBoost to detect vehcle 4 Nghtte Moton Vehcle Detecton Based on MILBoost Keeler, et al [7] proposed orgnally the dea for the ultple nstance learnng for handwrtten dgt recognton n 990 It was called Integrated Segentaton and Recognton (ISR), and t s the ey dea to provde a dfferent way n consttutng tranng saples Tranng saples are not sngletons, at the sae te they are n bags, where all of the saples n a bag share a label [8]; Saples are organzed nto postve bags of nstances and negatve bags of nstances, whch each bag ay contan a nuber of nstances [9] At least one nstance s postve (e object) n a postve bag, whle all nstances are negatve (e non-object) n a negatve bag In MILBoost, learnng ust sultaneously learn whch saples n the postve bags are postve along wth the paraeters of the classfer MILBoost can learn whch nstances n the postve bags are postve, along wth a bnary classfer [20] In ths paper, MILBoost s eployed for vehcle detecton wth non-algned tranng saples The MILBoostbased vehcle detecton proceeds as follows: Input: Gven dataset { X, } N y =, where X s tranng bags, X = { x, x2,, xj,, xn}, y s the score of the saple, and y {0,}, N s the nuber of all wea classfers A postve bag contans at least one postve saple Pc out K wea classfers and consst of strong classfer Update all N wea classfers n the pool wth data { xj, y } Intalze all strong classfer: H j = 0 for all, j for = to K do for = to N do We calculate the probablty that the j-th saple s postve n the -th bag as follow: P = σ ( H + h ( x )), () j j j where Pj = p( y xj ) = + exp( yj ) We calculate the probablty that the bag s postve as follow: P = ( pj ), (2) where P = p( y X) The lelhood assgned to a set of tranng bags s: j C = ( y log( p ) + ( y)log( ( p ), (3) Fndng the axu * fro N as the current optal wea classfer as follow: * = arg n C The * coe nto the strong classfer: *, (4) h ( x) h ( x), (5) H = H + h ( x), (6) j j Output: Strong classfer whch consst of K wea classfers as follow: H ( x) = h ( x), (7) where h s the wea classfer and can ae bnary predctons usng sgn( H K ( x )) In MILBoost, saples coe nto postve bags of nstances and negatve bags of nstances Each nstance x j s ndexed wth two ndces, where for the bag and j for the nstance wthn the bag All nstances n a bag share a bag label y Weght of each saple coposed of the weght of the bag and the weght of the saple n the bag The quantty of the saples can be nterpreted as a lelhood rato, whch soe (at least one) nstance s postve n a 95

4 bag P j s the probablty whch soe nstance s postve So the weght of saples n the bags s P j log C We calculate: wj =, and get weght of the yj bags w j Tranng n the ntal stages s the ey to a fast and effectve classfer Tranng and evaluatng has a drect pact on both the features selected and the approprate thresholds selected The result of the MILBoost learnng process s not only a saple classfer but also weghts of the saples The saples have hgh score n postve bags whch are assgned hgh weght The fnal classfer labels these saples to be postve The reanng saples have a low score n the postve bags, whch are assgned a low weght The fnal classfer classfes these saples as negatve saples that as they should be We tran a coplete MILBoost classfer and set the detecton threshold to acheve the desred false postve rates and false negatve rates Retran the ntal wea classfer so that a zero false negatve rate obtans on the saples, whch labeled postve by the full classfer Ths results n a sgnfcant ncrease n any saples to be pruned by the classfer Repeatng the process so that the second classfer s traned to yeld a zero false negatve rate on the reanng saples For the tas of nghtte ovng vehcle detecton, our goal s to classfy a new ovng vehcle age to a specfc ovng vehcle class Durng the nference stage, gven a testng ovng vehcle age, we can treat each aspect n the MILBoosted odel as one class of ovng vehcle For ovng vehcle detecton wth large aount of tranng data, ths would result n long tranng te In ths paper, we adopt a supervsed Algorth to tran MILBoost odel The supervsed tranng algorth not only aes the tranng ore effcent, but also proves the overall detecton accuracy sgnfcantly Each age has class label nforaton n the tranng ages, whch s portant for the classfcaton tas Here, we ae use of ths class label nforaton n the tranng ages for the learnng of the MILBoost odel, snce each age drectly corresponds to a certan nghtte ovng vehcle class on tran sets 5 Experental Results and Analyss The perforance of the proposed algorth was verfed by usng C++ and Matlab hybrd pleentaton on a PC wth Pentu 32 GHz processor and 3G RAM We captured nghtte traffc vdeos at nght, for dfferent traffc condtons, dfferent weather condtons and under dfferent lghtng condtons by usng Charge Coupled Devce (CCD) caeras, whch of the fraes per second (fsp) was 25 fsp and the resoluton of each vdeo was pxels Fg (a) shows typcal saples of nghtte traffc scenes Our proposed algorth can process 64 fsp and effectvely satsfy the deands of real-te processng The detecton effect correspondng to the typcal saples are shown n Fg (b) Fg (a) typcal saples of nghtte traffc scenes; (b) the detecton effect correspondng to the typcal saples To objectvely evaluate the perforance of the proposed algorth, we use three dfferent vdeos n nghtte typcal traffc scenes, whch are dsplayed n Table Table Vdeos n nghtte typcal traffc scenes Vdeos Vdeo te span (n) Weather condtons, lghtng condtons good weather, street laps ran, street lap ran, street laps Traffc condtons Nuber of vehcles sooth 50 sooth 80 crowded 640 We copared the real nuber of vehcles aganst the nuber of vehcles detected n nghtte typcal traffc scenes The experental results correspondng to the typcal saples are shown n Table 2 Vdeos Table 2 Experental data of our algorth Manual count of vehcles Algorth count of vehcles Accuracy (%) To exane the accuracy of our proposed vehcle detecton approach, we copare our ethod to the 96

5 ethod of Wan et al [3] n 20 and the ethod of Zhang et al [4] n 202 usng the sae data and the sae experental settngs The coparatve results of nghtte vehcle detecton are shown n Fg 2 3) Experents were perfored and evaluated the proposed ethod Experental results reveal that the proposed ethod perfors better than prevous ones n coparson wth state-of-the-art ethods and can detect the vehcle robustly n coplcated traffc scene Acnowledgents Ths wor was supported by Research Foundaton for Scence & Technology Offce of Hunan Provnce under Grant (No 202FJ302, No 202GK4006), by the Teachng Refor Foundaton of Hunan Provnce Ordnary College under Grant (No ) and by the Foundaton for Key Constructve Dscplne of Hunan Provnce References Fg 2 Coparson of recognton accuracy for three ethods As Fg 2 shows, our ethod proves the detecton accuraces It acheves 987 % average detecton rate, whereas the ethod of Wan et al n 20 obtan 9538 %, and the ethod of Zhang et al n 202 gets 9474 % Fg 2 shows that the ethod of Zhang et al n 202 and the ethod of Wan et al n 20 do not perfor as well as our proposed ethod Our proposed ethod can provde better vehcle detecton perforance for nghtte traffc survellance than other extng ethods The experental and coparatve results can deonstrate that our proposed algorth can qucly, effectvely and robustly detect vehcles n dfferent nghtte traffc envronents, such as reflectons on the road and street laps, nterfere wth vehcle detecton 6 Conclusons Movng vehcle detecton can provde sgnfcant advantage n self-guded vehcles, drver assstance systes, ntellgent parng systes, Intellgent Transportaton Systes (ITS), or n the easureent of traffc paraeters In ths paper, we present a novel ethod to detect the ovng vehcle The an contrbuton can be concluded as follows: Vehcle ) SIFT ethod was used for extractng ovng vehcle SIFT features SIFT s an excellent descrptor of features n ages and s one of the optal optons for feature-based age regstraton 2) MILBoost odel was used for nghtte ovng vehcle detecton In addton, n order to prove the detecton accuracy, the class label nforaton was used for the learnng of the MILBoost odel [] N D Matthews, P E An, D Charnley, et al, Vehcle detecton and recognton n grey scale agery, Control Engneerng Practce, Vol 4, Issue 4, 996, pp [2] L W Tsa, J W Hseh, K C Fan, Vehcle detecton usng noralzed color and edge ap, IEEE Transactons on Iage Processng, Vol 3, Issue 6, 2007, pp [3] M Vargas, J M Mlla, S L Toral, et al, An enhanced bacground estaton algorth for vehcle detecton n urban traffc scenes, IEEE Transactons on Vehcular Technology, Vol 8, Issue 59, 200, pp [4] W Wan, T Fang, S L, Vehcle detecton algorth based on lght parng and tracng at nghtte, Journal of Electronc Iagng, Vol 4, Issue 20, 20, pp [5] W Zhang, Q M J Wu, G Wang, et al, Tracng and parng vehcle headlght n nght scenes, IEEE Transactons on Intellgent Transportaton Systes, Vol, Issue 3, pp [6] S L, Research of feature desgn and slarty easureent n coputer vson, PhD Thess, Unversty of Scence and Technology of Chna, 200 [7] L Cheng, Target recognton ethod based on structure of local feature, PhD Thess, Unversty of Scence and Technology of Chna, 2009 [8] K Molajczy, C Schd, A perforance evaluaton of local descrptors, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol 0, Issue 27, 2005, pp [9] K Molajczy, T Tuytelaars, C Schd, et al, A coparson of affne regon detectors, Internatonal Journal of Coputer Vson, Vol, Issue 65, 2005, pp [0] M Douze, H Jegou, C Schd, An age-based approach to vdeo copy detecton wth spatoteporal post-flterng, IEEE Transactons on Multeda, Vol 4, Issue 2, 2008, pp [] X Zhang, et al, Socal age taggng usng graphbased renforceent on ult-type nterrelated objects, Sgnal Processng, Vol 8, Issue 93, 203 pp

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