A Video Vehicle Detection Algorithm Based on Improved Adaboost Algorithm Weiguang Liu and Qian Zhang*

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1 A Video Vehicle Deecion Algorihm Based on Improved Adaboos Algorihm Weiguang Liu and Qian Zhang* Zhongyuan Universiy of Technology, Zhengzhou , China *The corresponding auhor Keywords: Adaboos; Opical flow; Haar feaure; Vehicles deecion Absrac. Mos of he mehods of vehicles deecion could no reach he requiremens of real-ime and accuracy. As a soluion, a vehicles deecion algorihm was proposed based on improved Adaboos algorihm. The mehod of he algorihm is ha o use he mehod of Opical flow o deecs he video frame hen ges he moving zone and pick up he moving zone as he region of ineres. Deecing he region of ineres wih he classifiers raining by Haar feaure and he vehicle-face image samples o find ou he vehicle informaion from video frames. The resuls of he experimens show ha his algorihm can decrease he range of deecion and improved he speed and accuracy. Inroducion I's a grea ease he raffic pressure wih he ITS ( Inelligen Transporaion Sysem ) come ou. The deecion o he video vehicle is an imporan apar in he ITS.The radiional mehods are based on foreground ouline in general, using he background unchanging o achieve he moving arge, hen o deec. such as background differencing and frame difference [1]. Bu hese mehods are easier influenced by he ouside hings like shadow, cover and non-arge inerference, as well as feaure-based mehod like opical flow [2]. I akes much ime caused ha i need o mached in he whole image. The ITS need he deecion o be real-ime and accuracy, bu here is a cerain gap beween all he radiional mehods and he ITS demands. This paper propose a mehod which combine he opical flow and he improved adaboos o deec video vehicles. Firs of all, confirming he range of moion of he moving arge wih he mehod of opical flow, wipe of he irrelevan region, make he area where he moving arge has sayed as he region of ineres (ROI). Secondly, deecing he ROI by using he algorihm of adaboos and he car-face sample se, hen finish he video car deecion. The algorihm of his aricle reduce he scope of moving zone and shoren he ime, o avoid he possibiliy of deec he wrong arge by he none ROI in he video frame simulaneously. The algorihm can effecively enhance he speed and accuracy of he deecion. Opical Flow Mehod Hom and Schunck have pu forward he compuing mehod of opical flow mehod [3]. To dae he Lukas-Kanade mehod proposed by Bruce D. Lucas and Takeo Kanade has been he mos common opical flow mehod, named L-K opical flow mehod [4]. The main principle of opical flow mehod is finding ou a pixel poin in image, by seing is coordinaes as( xy,, ) a ime, hen he luminance of he pixel poin is E( x, y, ), and u( x, y) and v( x, y) can also be used o donae move dx dy componen u and v of opical flow of he pixel poin a horizonal direcion and verical d d direcion respecively. Afer ime, corresponding luminance of pixel poin ( xy, ) would become E( x x, y y, ). If ime is small o none, i can conclude ha he luminance of pixel poin has no changed ye, and we can work ou he following Eq.1: E( x, y, ) E( x x, y y, ) (1) If he luminance of pixel poin has changed, in accordance wih he Taylor expansion, we can work The auhors Published by Alanis Press 0544

2 ou he following Eq.2: E E E E( x x, y y, ) E( x, y, ) x y x y (2) When is close o zero, he basic opical flow consrain equaion is shown as he following Eq.3: E E x E y Egw x d y d (3) E E E And in he formula, w ( u, v). If Ex, Ey and E can be used o denoe he greyscale x y of pixel poin in image along wih gradien of direcion of x, y,, and we can work ou he following calculaion formula of opical flow Eq.4: Exu Eyv E 0 (4) We can find ou moion informaion of moving objec and load video frame, as shown in he Fig. 1 Figure 1. Moving objec and load video frame In line wih he moion informaion of moving objec in fig. 1, moion region in video frame can be obained, which can be regarded as ROI, by which i is easy o be esed by applying Adaboos algorihm. Adaboos Algorihm Adaboos algorihm is he advanced version of boosing algorihm [5]. The realizaion of algorihm is similar o cerain cascaded classifier. Several weak classifiers can be obained by raining firs, and corresponding weigh value shall be se on each weak classifier respecively, by which hey can be combined ino a srong classifier for furher es. Haar Feaure. In he raining phase of Adaboos algorihm, he feaure of Haar [6] has been used o calculae eigenvalue for furher judgemen in his conex. The common feaure of Haar includes edge feaure, line feaure and diagonal feaure, corresponding wih he wo-recangle, hree-recangle and four-recangle in he picure below, as shown in he Fig. 2: Figure 2. Haar Feaure Diagram In order o calculae he feaure value of Haar feaure, Viola has inroduced concep of inegral image, by which he compuing ime upon feaure value can be reduced and he deecion speed can be improved. We suppose he coordinae of one pixel poin can be donaed as ( xy,, ) is inegral image can be donaed as Aa ( x, y ). The sum of pixel value of all pixel poins in recangle area composed by poin A and base poin is he compuing mehod of Aa ( x, y ). Because of inegral image, we can calculae he sum of pixel value of all pixel poins in cerain recangle area in he image. The difference beween pixel poins in black area and ha in whie area in Haar recangle feaure is he eigenvalue. Adaboos Training. Adaboos algorihm is a kind of ieraive algorihm. Before raining, we have o The auhors Published by Alanis Press 0545

3 iniialize weigh value of all raining samples, seing all weigh values as he same, by which he firs weak classifier can be obained. In he following raining, he disribuion of weigh value of sample of each pracice is deermined by he resul reached by he pracice of he las ime. The ime when ieraion finishes, error dividing sample would ge a higher weigh value, in order o complee he adjusmen of weigh value. Therefore, he sample being divided ino wrong area will be highlighed by high value, by which a new disribuion will be formed. Above all, a new round of ieraion can be implemened, by which a new weak classifier can be obained. The quaniy of weak classifier is deermined by he number of ime of ieraion. Afer he ieraion has been finished, he obained weak classifiers can be used o combine ino a srong classifier in line wih heir weigh values. Before raining, he number of Haar feaure in raining child-window can be obained by calculaion, and feaure value can be calculaed ou as well. Moreover, a weak classifier h( x, f, p, ) can be obained by raining in allusion o each feaure f. 1, pf ( x) p h( x, f, p, ) (5) 0, oher refers o feaure, refers o hreshold value, p refers o direcion of inequaliy sign and x refers o deeced child-window in Eq.5. The essence of classifier by raining is o obain an opimal hreshold value upon each feaure, which is available o decrease error of classificaion upon raining sample o minimum. The raining procedures are as following, feaure value of each f has o be calculaed ou firs, and all he feaure values have o be sored from small o large. For each elemen in righ order, weighs of all posiive samples and T, weighs of all negaive samples andt, and weigh of posiive sample in fron of he elemen and S shall be calculaed, and weigh of negaive sample in fron of he elemen and S shall also be calculaed. The hreshold value can be chosen from he curren feaure value o feaure value of previous elemen, and he classificaion error of he hreshold value shall be: e min( S ( T S ), S ( T S )) (6) Afer ieraion of T imes, T weak classifier(s) can be obained. Srong classifier can be combined for furher judgemen in line wih Eq.7: 1 Cx ( ) 2 0, oher T T 1, a 1 h a 1 (7) h refers o he opimal weak classifier. And: a log (8) refers o he weighing error rae of corresponding weak classifier. The sum of esing resuls of all weak classifiers muliplying by corresponding weigh values can be regarded as srong classifier in order o obain final classificaion resul. Adaboos Deecing Tes of Vehicle by Applying Opical Flow Mehod By undersanding and realizing opical flow mehod and Adboos algorihm, deecing es of vehicle can be carried ou upon video frame by inegraing he above wo algorihms, specific flow char is as shown in Fig. 3: The auhors Published by Alanis Press 0546

4 Load video Ge The posiive and negaive samples Go he arge wih Opical flow mehod Haar feaure Exrac The ROI classifier deecion resul Figure 3. Improved Adaboos Algorihm Flow Char Experimenal Resul and Analysis The average speed by deecing is under 30ms, which is accessible o mee requiremen of real-ime monioring. Under he environmen wih no significan sheler and small angular deviaion, he accuracy rae of vehicle by applying algorihm menioned in his aricle can reach o 95% more. Compared wih radiional algorihm, veraciy of deecion has been improved grealy by selecing moion region as ROI. Tes has been carried ou upon he same video by applying Adaboos algorihm and algorihm menioned in his aricle respecively. A colleced video frame is as shown in fig. 4, and we can find ou ha here is leak deecion and false deecion upon vehicle in video in video frame respecively, by applying Adaboos algorihm for deecion Figure 4.. Tes resul upon applying Adaboos a frame of algorihm Figure 5. Tes resul of ROI acquired by applying opical flow mehod Figure 6. Tes resul upon applying he algorihm menioned in his aricle The auhors Published by Alanis Press 0547

5 By applying he algorihm menioned in his aricle, as shown in Fig. 5, Adaboos algorihm can be applied upon ROI in order o deec he vehicle in video frame correcly, afer applying he esing resul ino original video frame, we can conclude he esing resul as shown in Fig. 6.And he resul of compared wih oher mehods is as shown in Table 1: Table 1 Tesing Resul Algorihm number of vehicle number of deecion False deecion Leak deecion Accuracy rae Adaboos % Opical flow % Algorihm in his aricle % I is easily o find ou ha he required area for deecion by Adaboos algorihm has reduced grealy by selecing moion region of moving objec, by which i has perform well in false deecion and leak deecion and is accuracy rae has improved grealy. Conclusion In conclusion, he improved adaboos which combine he opical flow mehod has cerain advanages o he previous mehod. The algorihm can promoe he speed of he deecion and he accruacy, and i has imporan significance o ITS, i can apply o he ITS o provide beer service. References [1] J.J. Qu, Y.H. Xin. Conbined Coninuous Frame Difference wih Background Difference Mehod for Moving Objec Deecion [J].Aca Phoonica Sinica, 2014, 07: (In Chinese) [2] Y.M. Yang. Moving Objecs Tracking Based on Improved Opical Flow Mehod [J]. Compuer and Digial Engineering, 2011, 09: (In Chinese) [3] B K, SCHUNK B G.Deerming opical flow [J].Aricle Inelligence, 1981, 17(1-3): [4] uce D.LucasTakeo Kanade,An Ieraive Image Regisraion Technique wih an Applicaion o Sereo Vision.[C].Proceedings of Imaging Undersanding Workshop.1981, [5] HAPIRE, SINGER Y M. Improved boosing algorihm using confidenceraed predicion [J].Machine Learning, 1999, 37(3): [6] Viola P,Jlnes M J.Rapid objec deecion using a boosed cascade of simple feaures[c]//proceedings of he 2011 IEEE Compuer Sociey Conference on Compuer Vision and Paern Recogniion[s.l.]:IEEE,2011: [7] Y. Liu, H.H. Wang, Y.L. Xiang and P.L. Lu. An approach of real-ime vehicle deecion based on improved Adaboos algorihm and frame differencing rule[j].journal of Huazhong Universiy of Science and Technology(Nayre Science Ediion),2013,S1: (In Chinese) [8] X.H. Wang, J.A. Qin and L.L. Fang. Research on video vehicle deecion based on Adaboos classifiers of he ROI [J]. Journal of Liaoning Normal Univerisiy(Naural Science Ediion),2014,01: (In Chinese) [9] X.L. Li, A.H. Li and X.F. Bai. Moving Vehicles Deecion in Inelligen Transporaion Sysem Based on Opical Flow [J]. ELECTRO-OPTECHNOLOGY APPLICATION, 2010, 02: (In Chinese) [10] Z.W. Liu, X.D. Pan and H.C. Tan. Vehicle Deecion Based on Seled Scene Video [J]. Highway Engineering, 2013, 05: (In Chinese) The auhors Published by Alanis Press 0548