Person Re-identification Based on Color Histogram and Spatial. Configuration of Dominant Color Regions

Transcription

1 Person Re-dentfcaton Based on Color Hstogram and Spatal Confguraton of Domnant Color Regons Kwangchol Jang, Sokmn Han, Insong Km College of Computer Scence, KIM IL SUNG Unversty, Pyongyang, D.P.R of Korea Abstract There s a requrement to determne whether a gven person of nterest has already been observed over a network of cameras n vdeo survellance systems. A human appearance obtaned n one camera s usually dfferent from the ones obtaned n another camera due to dfference n llumnaton, pose and vewpont, camera parameters. Beng related to appearance-based approaches for person re-dentfcaton, we propose a novel method based on the domnant color hstogram and spatal confguraton of domnant color regons on human body parts. Domnant color hstogram and spatal confguraton of the domnant color regons based on domnant color descrptor(dcd) can be consdered to be robust to llumnaton and pose, vewpont changes. The proposed method s evaluated usng benchmark vdeo datasets. Expermental results usng the cumulatve matchng characterstc(cmc) curve demonstrate the effectveness of our approach for person re-dentfcaton. Keywords - person re-dentfcaton, domnant color descrptor, domnant color regon, color hstogram, mage retreval. 1. Introducton Person re-dentfcaton s to recognze the persons prevously observed by many cameras, and s to dentfy ndvduals among many canddates. Person re-dentfcaton needs technques of matchng features between ndvdual from a probe set and the correspondng template n a gallery set. Dependng on the number of avalable frames per ndvdual, the scenaros for person re-dentfcaton can be classfed by Sngle vs Sngle(SvsS), Multple vs Sngle(MvsS), Multple vs Multple(MvsM)[3]. On the other hand, the recent person re-dentfcaton approaches can be dvded nto non-learnng based methods, and learnng-based methods. And also human body can be subdvded wth respect to ts symmetry propertes. Ant-symmetry subdvdes nto head, torso and legs, whle symmetry can be used to dvde nto left and rght parts of human body. In [1], human body part detector s based on ffteen non-overlappng square cells of ndvdual body. And also n [2], frstly Haar-lke features are extracted from the full body, and then the body s dvded nto upper and lower part, each s descrbed by the MPEG-7 Domnant Color Descrptor. In [4], usng nterest ponts and Hessan-Affne nterest operator s proposed, and also AdaBoost-based learnng method [5], a matchng dstance learnng [8] s presented. Also usng Global Color Context(GCC) [6], MPEG-7 Domnant Color Descrptor(DCD)[12,13,14], Maxmal Stable Color Regon(MSCR) [9,10,11] and so on are proposed. 1

2 Our person re-dentfcaton approach s based on Domnant Color Descrptor(DCD), that s, Domnant Color Regons of human body parts. The rest of the paper s organzed as follows. Secton 2 ponts out the extractons of Domnant Color Descrptor and Domnant Color Regon, and secton 3 addresses the feature matchng and a framework for our proposed method. Expermental results and conclusons are gven n secton 4 and 5, respectvely. 2. Domnant Color Descrptor and Domnant Color Regon Extracton 2.1. Color Space Choce and Quantzaton Color feature s mportant n mage processng and recognton, vdeo survellance, and so on. Especally, for appearance-based person re-dentfcaton, color s the most expressve and powerful cue. The HSV color space provdes an ntutve representaton and approxmates the way n whch human perceves and manpulates the color. Ths color model s already represented by Munsell 3D space reference frame. In general, the color value can be transformed convenently from RGB to HSV [13]. The converson algorthm s shown n follow: C RGB =(R,G,B) s a color value of RGB color space, C HSV =(h,s,v) s the transformed color value of HSV color pace. r=r/255, g=g/255, b=b/255 (h,s,v [0,1]) MAX=max(r,g,b), MIN=mn(r,g,b) v=max, delta=max-min f (MAX==0) s = 0 ; else s=delta/max; f (MAX==MIN) h=-1; //Hue s undefned. (Achromatc color) else { f ( r==max && g MIN ) h = 60*(g-b)/delta; else f (r==max && g==min) h = *(g-b)/delta; else f (g==max) h = 60*(2.0+ (b-r)/delta); else h=60*(4.0+(r-g)/delta); } where for a color value (h,s,v) of HSV color space, h [0, 360], s [0,1], v [0,1]. The dmenson of the color hstogram of HSV color space used to descrbe the color features drectly, wll be very much for true-color mages especally, owng to the abundance of mage color nformaton. Therefore, t s essental to reduce the dmenson of HSV color space components. or HSV color space, we dvde hue H nto eght parts, saturaton S and ntensty V nto three parts respectvely n consderaton of the human eyes to dstngush. A non-nterval quantzaton algorthm of the HSV space s shown as follows [13]. 2

3 0 f h[316,20) 1 f h[20,40) 2 f h[40,75) 3 f h[75,155) H (1) 4 f h[155,190) 5 f h[190,270) 6 f h[270,295) 7 f h[295,316) 0 f s [0,0.2] S 1 f s (0.2,0.7] (2) 2 f s (0.7,1] 0 f v [0,0.2] V 1 f v (0.2,0.7] (3) 2 f v (0.7,1] where h [0,360], s [0,1], and v [0,1]. Accordng to the quantzaton levels as above, the H,S,V 3-dmensonal feature vector for dfferent pxels can be transformed nto 1-dmensonal feature vector C as follows: C =Q S Q V H+Q V S+V (4) where Q S, Q V are quantfed seres of S and V, respectvely, then Q S =Q V =3. C = 9H+3S+V (5) Such value C s called quantfed value of pxel. And all the number of possble quantfed values s 8*3*3=72, and also quantfed value C of pxel belongs to [0,71]. 0,1,,71 are called quantzaton levels for 1-dmensonal feature vector, and L=72 s called the number of quantzaton labels. or HSV color feature vector (0,0,0), just C=0, and for HSV color feature vector (7,2,2), C=9*7+3*2+2=71. As above, RGB 3-D color feature vector of each pxel s just transformed nto 1-D quantfed feature vector through RGB to HSV transformaton, and t s lmted to L levels. Such quantfcaton can be effectve n reducng the dmenson by effects of lght ntensty, but also reducng the computatonal tme and complexty Domnant Color Descrptor Extracton Domnant Color Descrptor(DCD) s defned as C, P, V, S, =1,2,,N [13]. Here C s th domnant color, P s the percentage for domnant color C, V s ts color varance, and also S s a sngle number that represents the overall spatal homogenety of the domnant color n the mage, and N s the domnant color's number. Now wthout consderaton of color varance V and domnant color spatal unformty S, the above expresson s smplfed as C, P}, P [0,1], { =1,2,,N. Also, based on the above quantfcaton, N=L=72. 3

4 On the other hand, the number of domnant colors can vary from mage to mage and a maxmum of eght domnant colors can be used to represent the regons n MPEG-7 [13,15]. g.1 shows DCD representaton of regons. Here each regon can be represented by two to four domnant colors. Thus a few domnant colors are enough to represent a regon. In fact, MPEG-7 recommends that a regon may have one to eght domnant colors, and a maxmum of eght domnant colors s extracted for a regon. (a) (b) (c) g.1. DCD representaton of regons. Regons n (a), (b) and (c) need 2, 3 and 4 centrod domnant colors. Such eght domnant colors are called centrod domnant colors, each descrptor of them s called centrod Domnant Color Descrptor(smplcty centrod DCD). And also, let M be the maxmum number of centrod domnant colors, M=8. or a gven mage, M centrod DCDs extracton algorthm s shown as follows. Input: I: gven mage, L: quantzaton level number (L=72) Output: I : set of M centrod DCDs of mage I I = ; Step 1. for all pxels of I, calculate the quantfed value C. C=9*H+3*S+V. Step 2. for =0,1,,L-1, calculate the normalzed hstogram P of color. I = I { ={,P }}; Step 3. for I (=0,,L-1), rearrange I n descendng order of P. Step 4. Leave only frst M domnant color descrptors as centrod DCDs n I. Step 5. for ={C,P } I, renormalze P. (=1,2,,M) 2.3. Extracton of Domnant Color Regons or mage I and ts M centrod DCDs I, let C I be a set of centrod domnant colors of mage I. C I ={C {C,P } I }, =1,,M where C s th centrod domnant color. A domnant color regon of mage I s a connected component of pxels wth ts correspondng domnant color. Therefore, for mage I, the extracton of domnant color regons can be just consdered beng 4

5 smlar to the connected component extracton of a grey mage. The domnant color regon extracton algorthm s shown as follows: Input: I: gven mage, C I : set of M centrod domnant colors of I Output: R: set of domnant color regons R= ; or all the centrod domnant colors, C C I, =1,2,,M, repeat the followng steps 1 to 3; Step 1. extract the set of pxels wth domnant color C, I C. Step 2. for C C C I, extract the set of all the connected components R R, R,..., R }. C C { 1 2 N Here, N s a number of the connected components wth domnant color C, C R s the kth k connected component wth the domnant color C, R I. (k [1,N ]) C k C Step 3. R R U. R C where R s the set of segmented domnant color regons, and each regon of R has the correspondng centrod DCD. And the relatvely small regons wth 1 to 4 pxels can be regarded as nose regons, and should be removed. To do ths, the thnng algorthm s appled to each regon of R extracted as above, and then the resultng empty regons wth no pxel are removed from R, just regardng as nose regon, and the relatvely large regons are left n R as before. The extracton of domnant color regons s shown n g.2. g.2. Extracton of DCRs. 3. eature Matchng In general, for person re-dentfcaton, a probe person pedestran mage A and gallery person pedestran mage set G B are gven. Person re-dentfcaton s to retreve the canddate pedestran mages of G B smlar to probe pedestran mage A and rearrange them n smlarty order. Here, t consders feature matchng between two mages: a probe pedestran mage A and a pedestran mage B of gallery set G B. It assumes that human full body s extracted from gven mage by foreground and background segmentaton beforehand. And then human full body s dvded to only two body parts: upper and lower body parts by ant-symmetry as presented n arenzena et al. [3]. The extracton of the 5

6 domnant color descrptors and domnant color regons s appled to human body parts. Consequently, the feature matchng for person re-dentfcaton s performed between the domnant color regons extracted from human body parts of two mages A and B Matchng Based on Domnant Color Hstogram Smlarty of Human Body Parts In general, the color cue s wdely used n person re-dentfcaton as well as mage retreval and so on. Especally, the color of clothng provdes the consderable nformaton to dentfy the ndvduals. The hstogram of clothng color s just a key feature wdely used n many applcatons for person re-dentfcaton, partcularly appearance-based approach. Peoples wear the clothes wth smlar or equal colors n upper and lower body parts: torso and legs, whle wear the clothes wth dfferent colors. In accordance wth clothng of human body parts: torso and legs, the color hstograms of each part can be smlar or dfferent. Here, notce that color for hstogram s regarded as domnant color. g.3 s shown the color hstogram smlarty accordng to clothng. g.3(a) s depcted that color hstograms of two full bodes are smlar, whle both color hstograms of upper parts and ones of lower parts, are dfferent respectvely. However, g.3(b) s presented that color hstograms of two full bodes are smlar, and ones of upper parts and lower parts are smlar too. (a) (b) g.3. Hstogram smlarty of dfferent mages. (a). smlar for full body, whle dfferent for upper and lower parts, respectvely. (b). smlar for full body, and also smlar for upper and lower parts, respectvely. Let A U, A L be sets of pxels n upper and lower part of probe mage A, respectvely. And also smlarly let B U, B L be sets of pxels n upper and lower part of gallery mage B, respectvely. And let A U, A L be sets of M centrod DCDs of A U and A L, respectvely. Also let B U, B L be sets of M centrod DCDs of B U and B L respectvely. That s, AU AL BU AU AU {{ C, P } 1,2,, M} AL AL {{ C, P } 1,2,, M} BU BU {{ C, P } 1,2,, M} BL BL BL {{ C, P } 1,2,, M} 6

7 where M=8, and also AL BU BL P, P, P, and P are the percentages of th quantfed A U domnant color A U AL C, BU BL C, C, C n A U, A L, B U, B L, respectvely, and also are normalzed. Smlar to hstogram ntersecton, the color hstogram smlarty d DCH (A,B) between two mages A and B, s defned as follows: d DCH ( A, B) d( A, B ) (1 ) d( A, B ) (6) U 71 0 U L AU BU d ( A, B ) mn( P, P ) (7) U U 71 0 AL BL d ( A, B ) mn( P, P ) (8) L L where d(a U,B U ) s the color hstogram smlarty between upper body parts of two mages A and B, and also d(a L,B L ) s the color hstogram smlarty between lower body parts of A and B. Both d(a U,B U ) and d(a L,B L ) are normalzed. Here, γ s a weghted coeffcent, 0<γ<1. d DCH (A,B) s just the weghted sum of color hstogram smlartes d(a U,B U ) and d(a L,B L ), and s also normalzed. The larger d(a U,B U ) s, the more smlar upper body parts of two mages A and B are. And also the larger d(a L,B L ) s, the more smlar lower body parts of two mages A and B are. Consequently, the larger d DCH (A,B) s, the more smlar two mages A and B are deemed to be. d DCH (A,B) can be regarded as color hstogram smlarty, related to statstcal domnant color confguraton of human clothng Matchng Based on Spatal Smlarty of Domnant Color Regons Let R A, R B be sets of domnant color regons of mages A and B, respectvely. R { A A r }, =1,2,,N A, R { B B r j }, j=1,2,,n B Beng based on dssmlarty, the spatal smlarty d DCR (A,B) based on domnant color regon between two mages A and B, s defned by dssmlarty as follows: d ) L DCR ( A, B) mn d R ( u, w (9) wr B, uc wc ura where u and w are domnant color regons of R A and R B, respectvely. u c and w c are domnant colors of u and w, respectvely. d R (u, w) s defned by the dssmlarty between two regons u and w as follows: d R (u, w)=βd y (u, w)+(1-β)d h (u, w) (10) d y (u, w)= u y - w y /H (11) d h (u, w)= u h - w h /H (12) where u y, w y are y components of center of mnmum boundng rectangles(mbrs) for regon u and w, respectvely. Smlarly, u h, w h are heghts of MBRs for regon u and w, respectvely. H s the normalzed heght of two mages A and B. In fact, the szes of two test mages are normalzed to be equal beforehand. Here, for each regon s MBR, x component of center and ts wdth are not 7

8 consdered, beng not robust to pose and vewpont changes. As shown n (11), (12), d y (u, w) s the dstance between the centers of MBRs for u and w, and d h (u, w) s the dfference between the heghts of MBRs for u and w. Both d y (u, w) and d h (u, w) are normalzed. Here, β s the weghted coeffcent, 0<β<1, therefore d R (u, w) s normalzed too. If two domnant color regons u and w, are closed on y axs and heghts of MBRs are smlar, d R (u, w) s small approxmately to be zero, consequently t can be regarded that they are deemed to be smlar. The smaller d DCR (A,B) s, the more smlar A and B are deemed to be. d DCR (A,B) s just regarded as a smlarty based on the spatal confguraton of domnant color regons related to the person clothng Integrated eature Matchng Smlarty between probe mage A and gallery mage B, s evaluated as follows: d(a,b)=αd DCH (A,B)+(1-α)d DCR (A,B) (13) where d(a,b) s the smlarty between two mages A and B. Here,α s a weghted coeffcent, 0<α<1, d(a,b) s normalzed. If for two mages A and B, the domnant color hstograms between upper parts are smlar and also the domnant color hstograms between lower parts are smlar, and also the spatal confguraton of domnant color regons of them s smlar, d(a,b) s relatvely larger, and also t can be regarded that they are deemed to be smlar. Through some experments, the parameters for d(a,b) are set to as follows: α=0.4<1, β=0.6<1, γ=0.55<1. Here, α=0.4<1 s that spatal confguraton feature d DCR s a lttle more mportant than color confguraton feature d DCH. β=0.6<1 s that d y denoted the closeness of domnant color regons s a lttle more mportant than d h related to the heghts of them,. And also, γ=0.55<1 s that clothng color confguraton of upper part: torso, s more complcated than ones of lower part: legs, and therefore d(a U,B U ) s a lttle more sgnfcant than d(a L,B L ). In g.4, the framework of our approach s shown. g.4. ramework of our approach. 8

9 4. Expermental Results To evaluate the performance of our approach, we conducted some experments wth the hghly challengng VIPeR dataset(sngle-shot scenaro), beng one of the benchmark datasets. The person re-dentfcaton performance can be presented usng the Cumulatve Matchng Characterstc(CMC) curve. The VIPeR dataset conssts of 632 person mage pars by two dfferent camera vews. g 5 shows the CMCs of our approach and others;el,sdal,and ERSVM. And also some of results: query mages and target mages, are shown n g 6. Here, query mages 1, 2, and 4 matched correctly n frst rank order, and query mage 3 ddn t. The result of CMCs presents that our approach s more effectve than other methods as above. g.5. CMCs of Our Approach and other methods for VIPeR dataset. 5. Conclusons In ths paper, we addressed the person re-dentfcaton based on the color and spatal confguratons of domnant color regons of human body parts. Proposed approach s a knd of appearance-based person re-dentfcaton. It s just based on the domnant color hstograms and the spatal dstrbutons of domnant color regons of clothng. Domnant color hstogram smlarty s related to color confguraton, and also smlarty of domnant color regons s related to spatal confguraton for our appearance-based approach. The experment results presented that our approach should be robust to pose, vewpont and llumnaton changes, and also more effectve than others. 9

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