Implementation of Robust HOG-SVM based Pedestrian Classification

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1 Implementaton of Robust HOG-SVM based Pedestran Classfcaton Reecha P. Yadav K.K.W.I.E.E.R Nashk Inda Vnuchackravarthy Senthamlarasu and Krshnan Kutty KPIT Technologes Ltd. Pune Inda Sunta P. Ugale K.K.W.I.E.E.R Nashk Inda ABSTRACT Achevng pedestran protecton by means of computer vson s not a new topc n the feld of computer vson research; however t s stll beng pursued wth renewed nterest because of the huge scope for performance mprovement n the exstng systems. Generally, the task of pedestran detecton (PD) nvolves stages such as pre-processng, ROI selecton, feature extracton, classfcaton, verfcaton/refnement and trackng. Of all the steps nvolved n the PD framework, the paper presents the work done towards mplementng the feature extracton and classfcaton stages n partcular. It s of paramount mportance that the extracted features from the mage should be robust and dstnct enough to help the classfer dstngush between a pedestran and a non-pedestran, whle a good classfcaton algorthm would go a long way n precsely dentfyng a pedestran as well as n smplfyng the verfcaton stage of the PD framework. The presented work focuses on the mplementaton of the Hstogram of Orented Gradents (HOG) features wth modfed parameters that can represent accurate ntrnsc nformaton of the mage. Classfcaton s acheved usng Support Vector Machne (SVM). However nstead of employng a readly avalable SVM lbrary, the lnear SVM mplemented uses the Sequental Mnmal Optmzaton (SMO) algorthm. The results observed by ths HOG-SVM combnaton show promse to be the best feature extracton cum classfcaton module for a full-fledged PD system. General Terms Computer Vson, Image processng. Keywords Pedestran detecton, Feature extracton, Classfcaton, HOG, SVM, SMO. 1. INTRODUCTION In vew of the fact that 22 % of the world s road traffc deaths occur among pedestrans [1], measures ntended to mnmze pedestran fataltes due to neglgence on part of the drver or other such unfavorable crcumstances, are becomng mportant. Pedestran Detecton Warnng System (PDWS) n automobles detects pedestrans n front of the vehcle and warns the drvers to take approprate decson. Such an advanced warnng system acheves pedestran safety by employng cameras along wth computer vson and mage processng algorthms. Generally, the task of pedestran detecton (PD) nvolves stages such as pre-processng, Regon of Interest (ROI) selecton, feature extracton, classfcaton and trackng [2]. Fgure 1 shows the block dagram of a general pedestran detecton framework. Pre-processng (e.g. mage smoothng, contrast enhancement etc.) s done on the nput mage/vdeo n order to ad the performance of the subsequent stages. The enhanced mage s then subjected to segmentaton to obtan a set of ROIs, whch n ths case are the regons n the mage whch are more lkely to contan a pedestran. Feature extracton stage ams at acqurng certan mage characterstcs, whch wll most meanngfully represent the pedestran nformaton n the mage. In a PDWS, the features so extracted help n classfyng the nput mage samples as those contanng pedestran or not. Lastly, trackng s carred out to mantan the pedestran estmates obtaned after classfcaton over the subsequent frames of a vdeo, under real-tme scenaro. The paper s amed towards mplementaton of a sub-task of the complete PDWS that of mplementng a robust featureclassfer ensemble whch can sgnfcantly contrbute to mprovng the overall PD accuracy. The presented work mplements the Hstogram of Orented Gradents (HOG) feature proposed n [3], wth modfcatons made to certan parameters to sut classfer performance as detaled n Secton 3 below. Classfcaton s acheved usng a Support Vector Machne (SVM). However as opposed to usng a readly avalable SVM lbrary (e.g. SVMLght) as done n [3], the lnear SVM s mplemented usng the Sequental Mnmal Optmzaton (SMO) algorthm as descrbed n [4]. The remander of ths paper s organzed as follows. Secton 2 provdes a succnct revew of the major feature-classfer approaches engaged n the past for PD. Secton 3 detals the HOG methodology and the proposed modfcaton. The detals of basc prncple of SVM and ts mplementaton usng SMO form the content n Secton 4. The results obtaned by executng the two blocks.e. HOG based feature extracton and SMO based classfcaton are elaborated upon n Secton 5. Fnally, concludng remarks have been derved and relevant future work proposed as part of the eplogue n Secton 6. 10

2 Fgure 1: Block dagram descrbng major modules n vson based pedestran detecton system [2] 2. LITERATURE SURVEY Ths secton derves help from the work done n [5] to provde a bref overvew of the feature extracton and the classfcaton methodologes adopted by varous researchers towards achevng PDWS. Amongst all the features employed across the lterature, the most dscrmnatve stand-alone feature s the HOG feature [3]. Much of ts advantage comes from ts ablty to relably capture the local edge/gradent nformaton; along wth a bult-n nvarance to local llumnaton condton. Almost all modern detectors employ HOG as a stand-alone detector or n combnaton wth some other features. For nstance, HOG n combnaton wth Local Bnary Patterns (LBP) and Local Ternary Patterns (LTP) has been used n [6], wth mproved gans n detecton results. Enhancements to HOG also nclude the works descrbed n [7], [8], [9], [10] amongst others. Some other shape-based features have also been successfully employed. Most notceable works amongst them, beng based on shape context [11], edgelet [12] and shapelet [13] features. Edgelets, whch are short lne segments/curves, act as effcent local shape descrptors (e.g. useful for descrbng the headshoulder curve) and hence provde robustness aganst occluson [2]. Shapelet features are based on the prncple of extractng gradents from the salent regons of an mage, whch are more lkely to contan a pedestran [11]. Both, shapelet and edgelet features are learned usng a boostngbased approach. Shape Context, smlar to HOG descrptors are based on locaton and orentaton of edges, however are represented usng a log-polar hstogram [11]. Covarance descrptors [7] are another popular approach for pedestran detecton. They employ a covarance matrx senstve to parameters such as postons, gradents, gray-level of pxels, etc. Features can also be extracted from optcal flow mages. Dalal et al. [14] obtaned the Hstogram of Orented Flow (HOF) features (descrbng moton) usng optcal flow mages. However, the results obtaned usng moton features showed only lttle mprovement over ts counterparts. Upon obtanng the feature space representaton of the ROIs, a classfer s employed to dvde ths feature space nto the class pedestran and non-pedestran. One of the popular classfers s Support Vector Machne (SVM). It solves a bnary classfcaton problem by defnng a decson boundary between two dstnct classes, so as to have maxmum margn between the classes. In case of non-lnear data, kernels can be appled to lnearze the data n some hgher dmenson, thus enablng ther lnear classfcaton. Lnear SVM gves good performance when used n conjuncton wth some dscrmnatve feature lke HOG or ts varants [3], [14]. Employng a kernel-based non-lnear SVM [15] yelds slghtly better results but at the cost of ncreased computaton tme and memory requrement. A general form of SVM, called as latent SVM, has also been used n part-based pedestran detecton scheme n [16]. Latency here refers to the ntally unknown part locatons n a partcular detecton. Boost-based classfers are another popular classfcaton approach. They combne varous weak learners, over a number of teratons to buld a robust classfer [17], [18]. Though the boostng framework requres more tme to tran, t s capable of makng real-tme detectons. Cascade classfers or a fuson of classfers can also be used to mprove detecton speed and accuracy by usng the output of a fast yet weak classfer, to drve a strong but slow classfer [19]. ANN (Artfcal Neural networks), wth ther ablty to store experental data, can also be employed for the classfcaton task. Multple layers of neurons allow for non-lnear decson boundares between classes. The scheme used n [20] employs ANN to model parts and occlusons. Another classfcaton approach n use currently s that of Decson forests [21]. Decson trees calculate the membershp to a partcular class by repeatedly parttonng a dataset nto unform subsets. Decson forests are obtaned by combnng the predctons of multple ndependent Decson Tree Models to obtan a sngle predcton. Output of ANN and decson forest strategy has been found to be at par wth each other [22]. However greater complexty ncurred n these approaches, advocates the usage of SVM, whch gves comparable performance wth a much smaller mplementaton complexty. 3. IMPLEMENTION OF HOG From the lterature survey performed, HOG emerged as the most successful stand-alone descrptor for supervsed classfcaton. Ths secton descrbes feature extracton as proposed n [3] and ts mplementaton. Fgure 2 shows the block dagram of the HOG extracton process. The followng sub-sectons detal each of the consttuent blocks. Fgure 2: Block dagram for extractng HOG from an nput mage 11

3 3.1 Gradent Computaton Fgure 3: A 3x3 mage regon and ts correspondng gradent kernel Gradent of an mage measures the change n the gray level for each pxel n the mage along wth the drecton of the change. If we represent the gradent as a vector, the length of the vector represents the gradent magntude, whle ts drecton gves the gradent orentaton. The gradent nformaton along horzontal and vertcal drecton of the mage are calculated by convolvng the gven gray-scale mage wth the gradent kernel [-1, 0, 1] and [-1, 0, 1] T respectvely, as shown n Fgure 3. It s clear that the horzontal gradent (I x ) s obtaned by takng the dfference between the column values (x-drecton), whle the vertcal gradent (I y ) nvolves dfference calculaton between the row values (y-drecton). For a pxel (x,y), the convoluton of kernel over the mage ntensty mathematcally mples equatons (1) and (2).The gradent magntude (m) and orentaton (θ) s calculated usng the equaton (3) and (4) respectvely. I x = I x + 1, y I x 1, y I Y = I x, y + 1 I x, y 1 (1) (2) Fgure 4: Gradent Orentaton Bnnng In ths way, the gradent orentaton of each pxel s weghted by ts magntude nto approprate bns of a cell-based hstogram. Ths ensures that the object shape s captured relably n terms of dstrbuton of local gradent (or edge) nformaton. Each cell contrbutes a 9 dmensonal (D) feature vector, thereby producng 9 x n feature vector for the gven nput mage. Reference [3] clams that a best detecton performance can be acheved wth a cell sze of 8x8 pxel regon. However, a mnmum cell sze of 16x16 pxel regon s chosen n the presented work to speed up the HOG computaton wthout much loss n the classfer performance. 3.3 Normalzaton Normalzaton s an mportant step n robust HOG extracton process. It helps HOG mantan ts dscrmnatve property and perform consstently even aganst parameters lke local llumnaton varatons and foreground-background contrast n the nput mage. Normalzaton s done usng block as a fundamental regon of operaton. Each block s a regon comprsng of a square array of 4 cells. Also, each new block s defned wth a 50% overlap wth the prevous block. m = I x 2 + I Y 2 (3) θ = tan 1 I y I x (4) In case of an nput color mage, the mage gradent s computed ndependently for each of the three consttuent color planes. However, for each pxel, the color channel correspondng to the maxmum gradent magntude contrbutes to the fnal gradent magntude and orentaton values for that pxel. 3.2 Spatal / Orentaton Bnnng In ths step, the nput mage s dvded nto n cells of equal area. For every pxel n the cell, the gradent orentaton and magntude are calculated and are accumulated nto a hstogram. The hstogram comprses of nne bns obtaned by equally dvdng the nterval o nto bns of nterval 20 o.consder the 8x8 cell regon as shown n Fgure 4. Assumng that the pxel (1,1) corresponds to a gradent orentaton value of 30 o, t can be mapped to the bn correspondng to the nterval o.in other words, the gradent magntude correspondng to the orentaton value of 30 o, wll be added to the hstogram bn of o for that pxel. Smlarly, the next pxel (1,2) wth a gradent orentaton value of 55 o, s mapped wth ts gradent magntude nto the bn correspondng to o n the hstogram of the cell under consderaton. Fgure 5: A sngle cell (, j) contrbutes to the fnal feature vector w.r.t four dfferent blocks Normalzaton ensures that the cell-based local gradent nformaton s nvarant to local llumnaton condtons. It can be done by calculatng a measure of gradent energy [16] usng the gradent magntudes obtaned n the prevous stage over each of the blocks defned over the nput mage. Each of the cell (, j) s then normalzed w.r.t the gradent energy measure calculated for each block contanng ths cell, as 12

4 descrbed n [16]. Fgure 5 depcts a cell (, j), whch s a part of four blocks. Hence, ths partcular cell s normalzed separately w.r.t the gradent energy calculated over each of the four blocks ndvdually. Thus, each such cell contrbutes a 9D vector four tmes to the fnal feature vector. In other words, each cell contrbutes a 36D feature vector (4x9D=36D vector). 4. IMPLEMENTION OF SVM SVM s a state-of-the-art classfcaton method and s wdely used n supervsed classfcaton n machne learnng applcatons. Apart from ts smplcty and accuracy, the SVM classfer s popular because of the ease wth whch t can deal wth hgh-dmensonal data. It performs bnary classfcaton by defnng a hyper-plane that classfes the nput data nto two classes. As shown n Fgure 6, SVM has two varants - Hard margn SVM and Soft-margn SVM. Hard margn SVM requres all data ponts to be classfed correctly nto ther respectve classes. However, the more popular soft-margn approach allows controlled msclassfcaton of dffcult or nosy examples usng a parameter C to acheve a maxmum margn lnear classfer by avodng over-fttng and hence, the usage of kernels. Fgure 6: Hard-margn (sold lne) and Soft-margn (dotted lne) SVM Classfer The presented work employs a soft-margn SVM due to ts ablty to deal wth non-lnear data lnearly. Obtanng the separatng hyper-plane, wth a maxmum margn between the two classes, amounts to solvng the maxmzaton problem of Equaton (5), gven the constrants defned by Equatons (6) and (7). 1 max L j y y jx. x j (5) 2 j y 0 (6) (7) 0 C Equaton (5) s a quadratc programmng (QP) problem n α, where α s called as Lagrange Multpler, whch s ntroduced to solve any constraned optmzaton problem. The varable x n Equatons (5) represents the feature vectors obtaned from the tranng data, whle y represents the class labels assocated wth each of the tranng samples. For the two class learnng problem of pedestran classfcaton, y s taken as +1 for postve tranng samples and -1 for negatve tranng samples. Equaton (5) can be solved effcently usng Sequental Mnmal Optmzaton (SMO) algorthm, as proposed by John Platt [4]. The man advantage of SMO algorthm comes from the fact that t avods the use of a tme-consumng numercal QP optmzaton. It solves the optmzaton problem by choosng n each teraton a par α and α j to update next, usng some heurstc. The heurstc employed s such that t tres to pck a par α and α j that wll help make the bggest progress towards the global maxmum [4]. The SMO algorthm proceeds to update the orgnal functon L(α), by optmzng w.r.t. the par α and α j, whle keepng other α k s (where k ~=,j) constant. The Karush-Kuhn-Tucker (KKT) [23] condtons are checked to test for the convergence of the SMO algorthm. KKT condtons are the condtons that are used to solve problems wth nequalty constrants. Reference [23] gves detaled dervaton of the KKT condtons as applcable to the SVM optmzaton problem of Equaton (5), gven the constrant defned by the nequalty n Equaton (7). Upon solvng Equaton (5), only those values of α come out to be non-zero, whch correspond to the tranng samples actng as support vectors for the separatng hyper-plane. The equaton of such a separatng hyper-plane s gven by Equaton (8), where w corresponds to the normal to the desred hyper-plane and b s the mnmum dstance from the orgn to the hyper-plane. w T x b 0 ( 8) w yx ( 9) T f ( u) w u b (10) The parameter w s calculated usng Equaton (9), whle b s computed as the average of the b values correspondng to all the tranng examples, as explaned n [23].For a gven unknown test vector (.e. a test mage segment) u, the sgn of f(u) as calculated usng Equaton (10) s used for classfyng between the two traned classes.e., f f(u) s postve, then u belongs to class +1 or otherwse. 5. RESULTS Intally, the HOG features were calculated for segments of pedestran and non-pedestran mages. The HOG vsualzaton n Fgure 7 and Fgure 8 s an approxmate representaton of the actual HOG descrptor. They show the most promnent edge correspondng to each cell for a pedestran and a nonpedestran mage respectvely. Ths edge nformaton s derved from the most weghted gradent orentaton bn correspondng to the hstogram of each cell. Fgure 7: Image (rght) showng most promnent edge correspondng to each cell for a pedestran mage (left [24]) usng HOG feature 13

5 Table 1: Parameters employed n tranng and testng of classfer Parameter Value Segment/Crop sze 120 x 48 No. of tranng mages 4000 (2000 postve and 2000 negatve) No. of postve test mages 1138 No. of negatve test mages 1138 Cell-sze 16x16 Feature vector length/cell 36 Fgure 8: Image (rght) showng most promnent edge correspondng to each cell for a non-pedestran mage (left [24]) usng HOG feature For better vsualzaton of the edge nformaton, the results shown n Fgure 7 and Fgure 8 were obtaned usng a cellsze of 8x8. However, whle calculatng the HOG features for the mage segments whch are nput to the classfer, the cellsze employed s 16x16. Ths s done to reduce the amount of calculatons nvolved n HOG computaton, at the same tme avodng any degradaton n the classfer performance. The SMO algorthm based SVM classfer s mplemented usng the pseudo-code as proposed n [4]. For the tranng and testng of the classfcaton stage, the CVC-02 Pedestran Dataset [24] s employed. It was chosen because t provdes a separate subset of database focused towards the dfferent tasks nvolved n a PDWS, namely canddate generaton, classfcaton and overall system evaluaton. Also, t replcates the cluttered background commonly seen by a PDWS n any urban scenaro. For tranng of classfer, t provdes 1016 cropped postve (or pedestran) mages and 7650 cropped negatve (or non-pedestran) mages. A classfer traned wth more tranng data would be better adept at accurately detectng a pedestran. Hence to brng n more pedestran pose scenaros the CVC-02 dataset employs mrror mages of the postve dataset. The presented work uses 1000 pedestran mages along wth ther correspondng mrror mages and 2000 non-pedestran mages for tranng of classfer. Fgure 9 and Fgure 10 show some example mages from both the postve and the negatve class of the CVC-02 Pedestran Dataset. For testng, the presented work employs a total of 1138 pedestran mages, ncludng ther mrror mage and an equal number of non-pedestran mages. All the mages n the CVC-02 dataset are used after rescalng them to 120x48 mage szes. Table 1 summarzes the mportant parameters pertanng to the tranng and testng of the classfer. To acheve a maxmum-margn lnear SVM classfer, the parameter C n the table s used to control the degree of msclassfcaton of some dffcult examples n the tranng data. The best classfer performance s obtaned when C value s taken as C (for controlled msclassfcaton) 0.09 Fgure 9: CVC-02 Pedestran Dataset Postve Tranng mages Fgure 10: CVC-02 Pedestran Dataset Negatve Tranng mages Fgure 11: Msclassfed pedestrans (False Negatves) Out of the 1138 postve test mages, 1079 cropped pedestran mages were classfed as pedestrans by the mplemented feature-classfer par, amountng to a true postve classfcaton accuracy of 95%. Fgure 11 depcts some of the pedestran test mages whch were not classfed correctly by the mplemented HOG-SVM par. The prmary reason for ther msclassfcaton could be the low resoluton levels of these mages, coupled wth the low contrast between foreground and background regons. Wth 1138 negatve mages, the classfer yelded a false postves classfcaton percentage of 10%.e., 116 non-pedestran mages were msclassfed as pedestrans. Table 2 gves a summary of the results obtaned, whle Fgure 12 provdes a graphcal representaton of the same. 14

6 Table 2: Classfcaton Results Classfcaton Total number of test mages Number of classfed mages Percentage Postve Negatve True False Classfcaton True Postves (TP) (Pedestrans classfed as pedestrans) 1138 Not Applcable (NA) 1079 NA 95% False Negatves (FN) (Pedestrans classfed as non-pedestrans) True Negatves (TN) (Nonpedestrans classfed as nonpedestrans) False Postves (FP) (Non-pedestrans classfed as pedestrans) 1138 NA NA 59 5 % NA NA 90% NA 1138 NA % Fgure 12: Graphcal representaton of classfcaton results Table 3: Classfcaton performance parameters Classfer Performance Parameters Precson (Postve predctons that are correct) Recall / Senstvty (Postve labeled segments that were predcted as postve) Specfcty (Negatve labeled segments that were predcted as negatve) Accuracy (Predctons that are correct) CVC-02-Classfer Test Dataset Formula Value Percentage TP / (TP+FP) TP / (TP+FN) TN / (TN+FP) (TP+TN)/ (TP + TN +FP+FN) True Postves False Negatves False Postves % % % % Table 3 evaluates the classfer performance usng some commonly used metrcs. From the graph n Fgure 12 and the parameter values obtaned n Table 3, t can be concluded that the mplemented HOG-SVM feature-classfer par shows promsng results, wth a hgh true postve classfcaton accuracy and mnmal number of msclassfcaton as shown n Fgure 11. The percentage of the pedestrans whch were wrongly classfed s represented n Fgure 12 as False negatves. In order to reduce the number of non-pedestran mages from beng classfed as pedestrans, a short revew of major verfcaton strateges employed n the lterature has been provded n [2]. Employng a verfcaton strategy wll help valdate the classfed pedestran ROIs, whle flterng out the false postves. 6. CONCLUSION AND FUTURE WORK: In ths paper, mplementaton of two key blocks n the pedestran detecton framework, namely feature extracton and classfcaton are presented. HOG features are mplemented for feature extracton, wth a cell-sze of 16x16 (for computatonal speed-up) and an effcent normalzaton strategy (for llumnaton nvarance). A soft-margn lnear SVM, based on the smple yet effcent SMO algorthm s used for mplementaton of the classfcaton module. The classfer uses a subset of the CVC-02 Pedestran Dataset, whch s specfcally amed at the tranng and testng of the classfcaton stage. The results obtaned show hgh pedestran classfcaton accuracy (TP) of 95% and an overall classfcaton accuracy (TP+TN) of 92%. Hence, the mplemented feature-classfer ensemble can act as a fast and robust buldng block for a complete PDWS. However, to further mprovse the results of the presented work, measures such as ncreasng the amount of tranng data of the classfer and usng a sutable verfcaton strategy to reduce the number of false postves ncurred can be undertaken. Future work can also be amed at the mplementaton of an equally effcent segmentaton approach, whch would complement the presented feature-classfer modules. 7. REFERENCES [1] World Health Organzaton (WHO) Global Status Report on Road Safety 2013: Supportng a Decade of Acton. 15

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