Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp METROLOGY AND MEASUREMENT SYSTEMS. Index , ISSN

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1 Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp METROLOGY AND MEASUREMENT SYSTEMS Index , ISSN HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE BRIGHTNESS FOR HUMAN DETECTION Marek Wójckowsk Gdańsk Unversty of Technology, Faculty of Electroncs, Telecommuncatons and Informatcs, G. Narutowcza 11/12, Gdańsk, Poland ( wujek@ue.et.pg.gda.pl, ) Abstract A modfcaton of the descrptor n a human detector usng Hstogram of Orented Gradents (HOG) and Support Vector Machne (SVM) s presented. The proposed modfcaton requres nsertng the values of average cell brghtness resultng n the ncrease of the descrptor length from 3780 to 3908 values, but t s easy to compute and nstantly gves 25% mprovement of the mss rate at 10 4 False Postves Per Wndow (FPPW). The modfcaton has been tested on two versons of HOG-based descrptors: the classc Dalal-Trggs and the modfed one, where, nstead of spatal Gaussan masks for blocks, an addtonal central cell has been used. The proposed modfcaton s sutable for hardware mplementatons of HOG-based detectors, enablng an ncrease of the detecton accuracy or resgnaton from the use of some hardware-unfrendly operatons, such as a spatal Gaussan mask. The results of testng ts nfluence on the brghtness changes of test mages are also presented. The descrptor may be used n sensor networks equpped wth hardware acceleraton of mage processng to detect humans n the mages. Keywords: dgtal mage processng, object detecton, human detecton Polsh Academy of Scences. All rghts reserved 1. Introducton Detecton of persons n mages s an mportant and challengng task needed for applcatons such as drvng assstance, autonomous drvng or vdeo survellance, where the pedestran detecton must be both robust and n real-tme. There are two man methods of person detecton: the sngle-scannng wndow and the part-based detector. Scannng wndow methods are based on varous feature descrptors, such as Hstogram of Orented Gradents (HOG) [1], Haar wavelet [2], Edge Orentaton Hstogram (EOH) [3, 4], and Local Bnary Pattern (LBP) [3, 4]. The descrptors are classfed by usng machne learnng technques, such as Support Vector Machne (SVM) [5, 6] or a boostng classfer. The SVM s a well-known method of classfcaton wth a sold mathematcal background, where the learnng phase s reasonably short, but the classfcaton stage requres a sgnfcant number of multplcatons and addtons. The SVM has been successfully appled to many dfferent problems [7, 8]. The boostng classfer conssts of a cascade of weak classfers, where early stages of the cascade reject most negatve data. Owng to ths, only a lmted number of samples traverse the full cascade, thus ths method requres a very long tme n the learnng stage and t s quck n the classfcaton stage. Part-based methods [9] manly use a deformable model, whch mproves the detecton performance. They generally work better at partal occlusons and for pedestrans n varous poses. Feature descrptors are used for detecton of parts of the model. The part-based detectors requre ncreased computatonal costs and t s more dffcult to mplement a real-tme robust applcaton, whle many successful real-tme applcatons usng a sldng wndow have been reported. Artcle hstory: receved on Aug. 05, 2015; accepted on Dec. 06, 2015; avalable onlne on Feb. 26, 2016; DOI:1515/mms

2 M. Wójckowsk: HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE Among varous feature descrptors, the HOG descrptor outperforms most other technques and s wdely used for pedestran detecton. The HOG descrptor used together wth the SVM classfer s one of the best-known human detecton methods. The HOG has been ntroduced n [10] and [1]; many modfcatons of t may be found n the lterature, ntended for mprovement of the detecton qualty or speed. Combnng the HOG descrptor wth boostng-based methods gves hgher classfcaton speeds than the SVM-based methods [11, 12]. In [13], the HOG descrptor combned wth Haar-lke features and the boosted cascade classfer has been proposed to obtan a better detecton accuracy and effcency. In [14], the HOG descrptor s combned wth mult-scale curvelet features for the full body detecton. The smultaneous use of HOG and LBP gves long descrptors but t also gves very good detecton results, as descrbed n [15] and [16]. Zeng et al. [17] use a two-stage cascade of rejecters: the HOG classfers and LBP classfers, to mprove processng long descrptors. To decrease the processng tme and acheve real-tme operaton, many algorthm modfcatons and acceleraton technques have been nvented. A wdely-used technque s the ntegral mage method [18], whch provdes the possblty of obtanng area-based descrptor values n a constant tme. Hardware mplementatons, such as the feature extracton accelerator VLSI [19], can be used n portable, on-board vehcle systems or sensor networks; hardware accelerators can also ncrease the effcency of computer-based solutons. However, not all algorthms can be effcently mplemented n hardware, therefore the researchers are searchng for ppelne-frendly methods, whch can be embedded nto the vson chps [20]. In ths paper, a modfcaton of the classc HOG descrptor s proposed. The man contrbuton of ths paper s the ntroducton of addtonal, easly calculated values to the descrptor, whch nstantly mproves the mss rate parameter of wndow detector. To provde clarty of the presented evaluatons, all results are compared wth those of the well-known and well-descrbed classc method presented n [1] and are tested usng the INRIA dataset [21]. 2. The descrptor Calculaton of the classc HOG descrptor begns wth dvdng an mage under the detecton wndow nto a dense grd of rectangular cells. For each cell a separate orentaton of gradents s calculated. The hstogram conssts of evenly spaced orentaton bns accumulatng the weghted votes of gradent magntude of each pxel belongng to the cell. In [1], 8 x 8 pxel cells and 9 bns for the orentaton range of degrees have been used. Addtonally, the cells are grouped nto blocks and for each block all cell hstograms are normalsed. The blocks are overlappng, so the same cell can be dfferently normalsed n several blocks. The descrptor s calculated usng all overlappng blocks from the mage detecton wndow. From the detecton wndow of sze 64 x 128 pxels and for a block of 2 x 2 cells, shfted by 8 pxels, 3780 features per detecton wndow are obtaned. The basc verson of HOG descrptor would not gve such good results, unless some addtonal technques are used, as proposed by Dalal and Trggs [1]: For colour mages, separate gradents for each colour are calculated, but only the gradent wth the largest norm s used. A Gaussan mask s used on each pxel of the block to down-weght the pxels near the edges. Each vote of gradent magntude s b-lnearly nterpolated nto neghbourng bns and n the same way s also dvded nto neghbourng cells. Ths procedure s called a tr-lnear nterpolaton. In ths paper, a modfcaton of the classc HOG descrptor s proposed, where an addtonal value I s ncluded n each cell n the descrptor, as shown n Fg. 1. I s the average brghtness of the cell, calculated usng an average of R, G and B pxel components. Usng the nfnty norm of R, G and B, nstead of an average for calculatng a cell s average brghtness, gves 28

3 Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp slghtly worse results. Calculaton of I can be done easly durng calculaton of the hstograms of gradents; the man dsadvantage s the ncrease of the descrptor length from 3780 to 3908 features (for the detecton wndow of sze 64 x 128 pxels and for a block of 2 x 2 cells, shfted by 8 pxels), whch can cause an ncreased processng tme. It s also possble to use I' = I I nstead of I, where Iavg s the average brghtness of the wndow, but the test results avg are smlar to those usng I. cell a c b d block HOG(a) I a HOG(b) I b HOG(c) I c HOG(d) I d Fg. 1. The proposed structure of descrptor wth the average brghtness value of each cell. I represents the average ntensty of pxels belongng to the cell = {a,b,c,d}. a c e b d + HOG(e) Fg. 2. The structure of descrptor usng the central cell approach appled n [16], nstead of a Gaussan spatal mask used n [1], wth the cells average brghtness values ncluded n the descrptor. In hardware mplementaton, the most challengng operatons needed for calculatng the HOG are: the Gaussan mask and the tr-lnear nterpolaton, snce they do not ft well n a ppelned style of hardware operaton and ntegral mage approach. In [16] an addtonal HOG of the cell centred n the orgnal block has been used to replace the spatal pxel weghtng, whch n fact mproved the overall detecton qualty. The proposed modfcaton of addng a brghtness-based value has also been tested wth the central cell approach presented n [16] nstead of the Gaussan spatal mask, whch enables easer hardware mplementaton. 3. Learnng and testng the classfers HOG(a) I a HOG(b) I b HOG(c) I c HOG(d) I d The proposed modfcaton of HOG descrptor has been used wth the lnear SVM for classfcaton of the analysed mages. The lnear SVM s based on solvng the optmsaton problem [5, 6]: subject to: mn w,b, ξ 1 w 2 y ( x w + b) 1+ ξ 0 T w + C L = 1 ξ, (1) ξ 0, = 1,..., L, (2) 29

4 M. Wójckowsk: HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE where {x, y} s the tranng data set of sze L and x ℜ D s the nput pont of D attrbutes wth the correspondng label y = 1 or +1. ξ s a postve slack varable, whch relaxes the constrants and allows for msclassfyng some ponts, when the problem s not fully lnearly separable. The varables w and b defne the optmal orentaton of the hyperplane, separatng the ponts belongng to two dfferent classes wth the soft error margn controlled by the parameter C. The mnmsaton problem (1), (2) s solved usng the teratve quadratc problem solver, where the termnaton crtera for the algorthm may be: the maxmum number of teratons and/or the tolerance error ε. The HOG descrptor has been combned wth the lnear SVM to obtan a classfer. In ths paper the value of the parameter controllng the error margn n SVM has been set to C =, whch enables easy comparson wth the results presented n [1], where the same value for C has been appled. For the experments, the SVM mplementaton from OpenCV lbrary verson has been used, wth the termnaton crteron ε = The SVM has been traned usng the INRIA data set (2416 postve examples and negatve examples, ncludng ther mrrored versons) n the same way as n [1],.e. the retranng phase has been completed usng hard tranng examples detected after the frst tranng. The examples of the tranng mages are shown n Fg. 3. Fg. 3. A few examples of postve mages contanng pedestrans (top row) and negatve, nonpedestran mages (bottom row) from the INRIA database [21]. For the testng, the INRIA test mages have been used wth 1132 postve examples and 453 non-pedestran negatve mages, where each negatve mage has been extensvely searched wth 8 pxel shft of the test wndow and 1.2 x scale down factor of the mage. The testng procedure was the same as n [1]. Usually the Recever Operatng Characterstcs (ROC) curves are used to quantfy the performance of detectors, based on the classfcaton return values, whch are the sgned dstances to the margn n the 2-class SVM classfer. The shape of ROC curves does not enable easy dstngushng of small probabltes, so n the further consderatons the RecallPrecson (RP) and Detecton Error Tradeoff (DET) curves wll be used, whch contan the same nformaton as the ROC curves. The RP curve plots precson versus recall on a log-log scale. Precson and recall are defned as: Precson = Recall = TP, TP + FP (3) TP, TP + FN (4) where: TP the number of wndows where there was a person and a person has been detected; 30

5 Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp FP the number of wndows where there was no person, but the detector ndcated the presence of a person; TN the number of wndows where there was no person and the detector dd not detect any person; FN the number of wndows where there was a person but the detector dd not detect any person. The RP curves are presented n Fg. 4 for three classfers: the Dalal-Trggs classfer from [1] (HOG) and the classfers from Fg. 1 and Fg Precson HOG HOG + Cell Brghtness HOG + Cell Brghtness + Central Cell Fg. 4. Recall-Precson curves for the classfer usng the descrptor from Fg. 1 (HOG + Cell Brghtness) and from Fg. 2 (HOG + Cell Brghtness + Central Cell). For comparson, the results from the Dalal-Trggs method [1] are also gven (HOG). All tests have been performed usng the INRIA Person dataset wth re-tranng on hard examples. The negatve test mages have been scanned usng 1.2 x rescale factor and 8-pxel wndow shft (the same procedure as descrbed n [1]). Recall HOG HOG + Cell Brghtness HOG + Cell Brghtness + Central Cell False Postves Per Wndow (FPPW) Fg. 5. Detecton Error Trade-off curves for the classfer usng the descrptor from Fg. 1 (HOG + Cell Brghtness) and from Fg. 2 (HOG + Cell Brghtness + Central Cell). For comparson, the results from the Dalal-Trggs method [1] are also gven (HOG). All tests have been performed usng the INRIA Person dataset wth re-tranng on hard examples. The negatve test mages have been scanned usng 1.2x rescale factor and 8-pxel wndow shft (the same procedure as descrbed n [1]). 31

6 M. Wójckowsk: HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE In Fg. 5 the DET curves are shown, whch use the measures: the MssRate and False Postves Per Wndow (FPPW), defned as follows: = 1 = =, (5). (6) The test results presented n Fg. 5 show 25% mprovement of the mss rate at 10 4 FPPW for the proposed descrptor, whch s equvalent to three tmes better FPPW at the same mss rate. It must be noted that part of the mss rate mprovement has been acheved by usng the central cell approach. To seek an optmal value of the parameter C, k-fold cross-valdaton wth the INRIA tran dataset and k = 9 has been appled, where C has been rased from the value C0 = up to Cj = 20 wth step S = 1.2 n teratons j = 0, 1,..., n, where each next value of Cj has been calculated as: Cj+1 = S Cj. (7) As the result of ths search, the new value of C = has been obtaned, for whch the test set error s mnmal. However, the performance of the classfers wth the new value of C was smlar to the prevous results (see Fg. 6), therefore the prevous value C = has been used n all further calculatons presented n ths paper. a) b) c) False Postves Per Wndow False Postves Per Wndow False Postves Per Wndow Fg. 6. Detecton Error Trade-off curves for the classfers learned wth the SVM s parameter value C = (dashed lne) and the value C = obtaned from k-fold cross-valdaton and sweepng (sold lne) usng: a) the descrptor from the Dalal-Trggs method [1]; b) the descrptor from Fg. 1; c) the descrptor from Fg. 2. Usng brghtness values drectly n the descrptor mght suggest that the descrptor has lost ts brghtness-nvarance. To test the behavour of the descrptor, a test set has been prepared, contanng the transformed INRIA test mages, where the ntenstes of the postve test mages have been randomly changed accordng to the equaton: I new = α I + β, (8) where: I and Inew the ntenstes of the mage before and after transformaton, respectvely; α, β the coeffcents, randomly changed for each mage, α has been changed n the range from 0.5 to 3 and β from 50 to 100. The results of the test usng the brghtness-transformed postve test mages are presented n Fg

7 Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp HOG HOG + Cell Brghtness HOG + Cell Brghtness + Central Cell False Postves Per Wndow (FPPW) Fg. 7. The test results showng the nfluence of brghtness changes accordng to (8) for the proposed descrptor from Fg. 1 (HOG + Cell Brghtness) and from Fg. 2 (HOG + Cell Brghtness + Central Cell). For comparson, the results from the Dalal-Trggs descrptor [1] are also gven (HOG). Tranng and testng have been performed on the INRIA Person dataset usng the same procedure as descrbed n [1] wth randomly changed brghtness of the postve test mages. In another test, nstead of unformly changng the brghtness of the whole wndow, the brghtness has been randomly changed at random regons of the mage. Frst, each tested wndow has been dvded nto two parts by a dagonal lne at a varable poston and angle, then the brghtness of a randomly chosen part has been changed accordng to (8) wth the coeffcents changed randomly for each wndow. The random brghtness change has been also appled to the randomly selected rectangular areas of the wndow (wth the random number of rectangles from 1 to 4). The examples of mages after those transformatons are shown n Fg. 8. The DET curves presentng the performance of detectors for the manpulated mages are shown n Fg. 9. The results from both tests wth the use of mages wth randomly changed brghtness show that the DET curves n Fg. 7 and Fg. 9 are always above the DET curve for the classc HOG, provng that the proposed modfcaton gves better results than the classc HOG descrptor. Fg. 8 Examples of the postve test mages from the INRIA database wth randomly changed brghtness n random areas. 33

8 M. Wójckowsk: HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE HOG HOG + Cell Brghtness HOG + Cell Brghtness + Central Cell False Postves Per Wndow (FPPW) Fg. 9. The test results showng the nfluence of random brghtness change n random rectangular regons for the proposed descrptor from Fg. 1 (HOG + Cell Brghtness) and from Fg. 2 (HOG + Cell Brghtness + Central Cell). For comparson, the results from the Dalal-Trggs descrptor [1] are also gven (HOG). Tranng and testng have been performed on the INRIA Person dataset usng the same procedure as descrbed n [1], where the postve test mages have randomly changed brghtness of randomly selected regons of the mage. The calculaton tmes of the proposed soluton are presented n Table 1. Due to the ncluson of the brghtness data nto the descrptor from Fg. 1, the classfcaton tme of a sngle detecton wndow (measured as the pure classfcaton tme, not ncludng the tme spent on readng the mage from dsk and wrtng the results) has ncreased by approx. 1% comparng wth that for the classc HOG descrptor. The descrptor from Fg. 2 s by 2% slower, but t has the potental to be more effectve n hardware, ppelned mplementaton. Table 1. Comparson of the calculaton tmes of SVM mplementatons. The descrptors have been wrtten n C++ usng OpenCV verson of the SVM lbrares. The evaluatons have been made usng a PC computer wth Intel GHz and 64-bt Wndows 7 operatng system. Pxel ntensty values have been represented as 8-bt unsgned numbers. For the magntude of the gradent, 64-bt floats have been used; the angle has been calculated n degrees and saved as a 32-bt nteger. The hstograms have been represented as STL vectors of 64-bt floatng-pont numbers. Unts Dalal-Trggs HOG descrptor [1] HOG + Cell Brghtness descrptor from Fg. 1. HOG + Cell Brghtness + Central Cell descrptor from Fg. 2. Descrptor length (the number of values) Average classfcaton tme of sngle test wndow [ms] Total learnng tme (26776 mages), ncludng readng [s] the mage fles from dsk, calculaton of the descrptor and savng the results to dsk Total classfcaton tme (10192 mages), ncludng readng the mage fles from dsk, calculatng the descrptor and savng the results to dsk [s] Operaton of the descrptor s shown n Fg. 10, where a dense scan of an mage contanng standng persons has been made. As can be seen, the detector usng the proposed descrptor gves more TP detectons for almost each person on the mage. At the same tme, a few FP 34

9 Metrol. Meas. Syst., Vol. XXIII (2016), No. 1, pp detectons are skpped n Fg. 10b the frst person to the left and the frst persons to the rght have some false detectons, whch are correctly not detected n Fg. 10d. a) b) c) d) Fg. 10. An example of dense scan detecton results: a) the nput mage; b) the detecton results from the Dalal-Trggs s descrptor; c) the detecton results from the descrptor from Fg. 1; d) the detecton results from the descrptor from Fg. 2. The overlappng detectons have not been merged (.e., usng non-maxmum suppresson) to show all detectons. The mage of sze 1474 x 828 pxels has been scanned wth the movng wndow wth the step of 3 pxels and rescaled down wth the factor Conclusons The man contrbuton of the author s the dea of usng addtonal nformaton n the HOG descrptor based on average pxel ntensty. Ths smple modfcaton slghtly ncreases the length of the descrptor but t results n a sgnfcant mprovement of the mss rate of the wndow detector. The proposed dea has been compared wth the well-known HOG descrptor descrbed n [1], also the modfcaton based on the dea of usng a central cell nstead of a spatal Gauss mask [16] has been made. The test results show that ths modfcaton gves valuable hnts to the SVM classfer, resultng n the mss rate mprovement by 25% at 10 4 FPPW over the orgnal verson of the HOG method, at the expense of up to 2% ncrease of the calculaton tme. Due to a shallow shape of DET curves, such an mprovement n the mss rate s equvalent to 3 tmes better FPPW at the same mss rate. It has also been shown that the proposed modfcaton, despte the fact that t uses the pxel ntenstes n the descrptor n addton to gradents, t stll provdes the mprovement for mages wth randomly changed brghtness. Ths shows that the brghtness values present n the proposed descrptor contan addtonal nformaton that helps the SVM to dscrmnate between postve and negatve samples and may be consdered as another procedure to mprove the mss rate of the detectors. It s hghly probable that addng the brghtness-based values can mprove many other descrptors based on the HOG method. 35

10 M. Wójckowsk: HISTOGRAM OF ORIENTED GRADIENTS WITH CELL AVERAGE References [1] Dalal, N., Trggs, B. (2005). Hstograms of orented gradents for human detecton. Proc. IEEE Int. Conf. Comput. Vson Pattern Recognt., [2] Vola, P., Jones, M. (2001). Rapd object detecton usng a boosted cascade of smple features. Proc. IEEE Int. Conf. Comput. Vson Pattern Recognt., [3] Ma, Y., Chen, X., Jn, L., Chen, G. (2011). A monocular human detecton system based on EOH and orented LBP features. Proc. 7th Int. Conf. Adv. Vsual Comput., I, [4] Ma, Y., Deng, L., Chen, X., Guo, N. (2013). Integratng Orentaton Cue Wth EOH-OLBP-Based Multlevel Features for Human Detecton. IEEE Trans. Crcuts Syst. Vdeo Technol., 23(10), [5] Boser, B.E., Guyon, I., Vapnk, V. (1992). A tranng algorthm for optmal margn classfers. Proc. Ffth Annual Workshop on Computatonal Learnng Theory, ACM Press, [6] Cortes, C., Vapnk, V. (1995). Support-vector network. Machne Learnng, 20, [7] Cu, J., Wang, Y., (2010). A novel approach of analog fault classfcaton usng a Support Vector Machnes classfer. Metrol. Meas. Syst., 17(4), [8] Wójtowcz, B., Dobrowolsk, A., Tomczykewcz, K., (2015). Fall detector usng dscrete wavelet decomposton and SVM classfer. Metrol. Meas. Syst., 22(2), [9] Zhang, H., Ba, X., Zhou, J., Cheng, J., Zhao H. (2013). Object Detecton va Structural Feature Selecton and Shape Model. IEEE Trans. Image Process., 22(12), [10] Lowe, D.G. (2004). Dstnctve Image Features from Scale-Invarant Keyponts. Int. Journal of Comput. Vson, 60(2), [11] Zhu, Q., Yeh, M.C., Cheng, K.T., Avdan, S. (2006). Fast human detecton usng a cascade of hstograms of orented gradents. Proc. IEEE Int. Conf. Comput. Vson Pattern Recognt., 2, [12] Zeng, H.C., Huang, S.H., La, S.H. (2008). Real-tme vdeo survellance based on combnng foreground extracton and human detecton. Proc. 14th Int. Multmeda Modelng Conf., MMM 2008, Kyoto, Japan, [13] Chen, Y.T., Chen, C.S. (2008). Fast human detecton usng a novel boosted cascadng structure wth meta stages. IEEE Trans. Image Process., 17(8), [14] Cheng, H.Y., Zeng, Y.J., Lee C.C., Hsu S.H. (2013). Segmentaton of Pedestrans wth Confdence Level Computaton. Journal of Sgnal Processng Systems, 72(2), [15] Wang, X., Han, T. X., Yan, S. (2009). An HOG-LBP human detector wth partal occluson handlng. Proc. IEEE Int. Conf. on Comput. Vson, ICCV 2009, Kyoto, [16] Gesmann, P., Knoll, A. (2010). Speedng Up HOG and LBP Features for Pedestran Detecton by Multresoluton Technques. Proc. 6th Int. Symposum Advances n Vsual Computng, ISVC 2010, Las Vegas, NV, USA, [17] Zeng, C., Ma, H., Mng, A. (2010). Fast human detecton usng m-svm and a cascade of HOG-LBP features. Proc. 17th IEEE Int. Conf. on Image Processng (ICIP), [18] Crow, F. (1984). Summed-area tables for texture mappng. Proc. of SIGGRAPH, 18(3), [19] Takag, K., Tanaka, K., Izum, S., Kawaguch, H., Yoshmoto, M. (2014). A Real-tme Scalable Object Detecton System usng Low-power HOG Accelerator VLSI. Journal of Sgnal Processng Systems. [20] Jendernalk, W., Blakewcz, G., Handkewcz, A., Melosk, M. (2013). Analogue CMOS ASICs n mage processng systems. Metrol. Meas. Syst., 20(4), [21] Everngham, M., Zsserman, A., Wllams, C.K.I., Van Gool, L. (2006). The PASCAL Vsual Object Classes Challenge 2006 (VOC 2006) Results. Techncal Report, Unv. of Oxford. [22] Chang, C.C., Ln, C.J. (2011). LIBSVM: a lbrary for support vector machnes. ACM Transactons on Intellgent Systems and Technology, 2(3) 27:1 27:27. 36