Upper Body Tracking for Human-Machine Interaction with a Moving Camera

Transcription

1 The 2009 IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems Ocober -5, 2009 S. Louis, USA Upper Body Tracking for Human-Machine Ineracion wih a Moving Camera Yi-Ru Chen, Cheng-Ming Huang, and Li-Chen Fu, IEEE fellow Absrac This research presens an upper body racking mehod wih a monocular camera. The human model is defined in a high dimensional sae space. We hereby propose a hierarchical srucure model o solve he racking problem by SIR (Sampling Imporance Resampling) paricle filer wih pariioned sampling. The image spaial and emporal informaion is used o rack he human body and esimae he human posure. When doing he human-machine ineracion, a saic monocular camera may no ge pleny of informaion from 2D images, so we mus move he camera plaform o a beer posiion for acquiring more enriched image informaion. The proposed upper body racking echnique will hen adjus o esimaing he human posure during he camera moving. To validae he effeciveness of he proposed racking approach, exensive experimens have been performed, of which he resul appear o be quie promising. I. ITRODUCTIO ITH he rapid developmen of inexpensive and high Wechnology machines, robos are more and more popularly seen o be equipped wih differen capabiliies. Human-machine ineracion becomes an increasingly imporan issue. There are many ways for humans o communicae wih he robos, like using he normal compuer device, such as keypad and mouse, microphone (for sound), camera (for vision), force sensor (for ouch), laser and ulrasound (for deph informaion). In recen years, vision-based analysis on human s moion analysis has become a popular problem due o a number of poenial applicaions, like biomechanical analysis, compuer animaion, surveillance sysem, pedesrian alarm on approaching vehicles, human-machine inerface, human-robo ineracion, ec. This research field has been widely surveyed in he lieraure [-5]. In paricular, Moeslund e al. [3] presened an overview which decomposes he flow of he visual sysem ino several processes, including he iniializaion, racking procedure, pose esimaion, and recogniion. The firs sep of heir proposed algorihm o analyze human s moion is human deecion. In differen siuaions, we may pay aenion on he differen pars of he body. The work in [5] gave an overview of human racking via racking of some body pars such as face, hands, fingers, ec. Manuscrip received March, This work was suppored bby aional Science Council, Taiwan, under Gran SC E Y. R. Chen and C. M. Huang are wih he Deparmen of Elecrical Engineering, aional Taiwan Universiy, Taiwan, ROC. L. C. Fu is wih he Deparmen of Elecrical Engineering and Compuer Science and Informaion Engineering, aional Taiwan Universiy, Taiwan, ROC ( lichen@nu.edu.w). The resuls of racking differen body pars may lead o differen applicaions. For example, racking he full human body can be applied in securiy-sensiive environmen, such as banks, parking los and airpors. Leg racking can be used in gai recogniion, human idenificaion and rajecory analysis. Hand racking has a number of applicaions such as gesure and sign language recogniion for human-compuer ineracion. Face racking can be uilized for human idenificaion, meeing analysis, and camera s auomaed focus. In fac, he upper body racking, which is also he focus of his paper, is an imporan subjec in he field of human-robo ineracion especially when home robos come ino play, because he image informaion will be sufficien o analyze he user informaion, like face and arm. Thus, he upper body posure analysis becomes an insrucion during he human-robo ineracion. There are many ways for human or posure deecion. In radiion, he works esimae he human by marking he human body pars [6] or by asking he human o wear some special clohes [7]. However, his is inconvenien and can only be used in special siuaions wih he complee equipmens being insalled. To improve he mehods, many researches provide oher alernaive approaches wihou relying on markers worn or aached ono he human body. The learning-based mehod [8] is applied o deec he human posiion. Using he background subracion or foreground segmenaion echniques, he silhouee-based mehods [9] can be uilized o deec humans wih a saic camera. Lasly, he dense dispariy map is consruced for esimaing human posure by using he 3D informaion from a muli-camera sysem [0]. In order o combine he posure deecion algorihm wih he human-robo inerface, he racking sysem may no be able o assume ha he environmen is wih saic background and camera since boh he human and he camera mouned on he robo may move while ineracing. Wihou such assumpion, i becomes more difficul for he sysem o fulfill he racking objecive since now he sysem can no always rely on use of background subracion o ge a foreground silhouee. This paper presens a vision-based racking algorihm ha racks he 3D upper body posure of a human. Concerning he processing ime, applying some deecion echnique in each frame during he course of racking a human generally will no be efficien. Combining hose racking algorihms can reduce he complexiy and save he compuaion cos. Besides, racking human beings is one of he mos difficul asks, because human s moions are fas, nonlinear, unpredicable, /09/$ IEEE 97

2 and here are so many kinds of possible human posure. So far in he lieraure, he paricle filer, which is a popular racking algorihm, can successfully solve he non-gaussian sae esimaion problems in nonlinear sysems. As a consequence, his paper deals wih he human racking problem by employing he paricle filer as a basic racking echnique. Besides ha, we also adop he opical flow o find he moion par and he movemen in he image so ha he human model can be robusly idenified, which in urn will faciliae accomplishmen of racking. This paper is organized as follows. In Secion II, we firs inroduce our human model and paricle filer. And hen in Secion III, we describe he racking algorihm and he feaure we used in his paper. In Secion IV, we describe he scenario of human-robo ineracion wih monocular camera. In Secion V, we demonsrae some experimenal resul o validae he effeciveness of he proposed racking approach. Finally, we conclude his paper in Secion VI wih some relevan discussion. II. HUMA MODEL AD TRACKIG FILTER A. Human Model Definiion As discussed in he previous work [5], here are a few kinds of human model which can be caegorized ino 2D models and 3D models. In 2D models, hey usually ry o fi he body conour by ellipse and recangle. There are wo kinds of 3D models, namely, sick model and volumeric model. The sick model also named skeleon model in which skeleons are conneced by he joins. The volumeric model defines a 3D model o represen he 2D conour, such as a cylinder o represen a recangle, a cone o represen a riangle, and a sphere o represen a circle. Here we use 3D sick model o deermine he join posiions. As shown in Fig., p 0 is he origin of he coordinae sysem. In general, he size of every par is proporional o he face size. We model he head as an ellipse whose raio beween lengh of he minor axis o ha of he major axis is :.2. Bu he shoulder widh and arm lengh are more differen from one person o anoher. In order o improve he accuracy of he model, we need an iniial pose as shown in Fig. o decide he shoulder widh and he arm lengh of one specific user. Afer aking he proper size from he user, we can sar he racking sysem. The human model consiss of head, shoulders, elbows and wriss. ex, we use he paricle filer o esimae he paricle sae which comprises join angles of each body par. The shoulder join ( θ, φ, ρ ) can be represened in he spherical coordinae sysem S which is cenered a shoulder posiion. We perform coordinae ransformaion hrough he following operaions. Firs, we roae he x-axis in S wih angle θ abou he y-axis, and hen roae x-axis wih angle φ abou y -axis, finally roae y-axis wih angle ρ abou z-axis. Afer ha, we ranslae he curren coordinae sysem o elbow coordinae sysem H, and roae y-axis abou z-axis wih angle γ. The deails of he ransformaion menioned above all are illusraed in Fig. 2 and 3, wih all he relaionships and limiaions of angles provided. Fig.. Human model wih he iniial posure. Fig. 2. Join angle for he shoulder wih limied range. Fig. 3. Join angle for he elbow wih limied range. The sick beween p 5 and p 7 lies on he plane decided by he roaion angle ρ L. The 3D human model will be projeced ono he image plane, and hen we evaluae he join angles o fi he human model. We have o decide he relaionships of posiion, scale and orienaion beween he human model and he image plane, as shown in Fig. 4. The posiion of p 0 on he image coordinae and he scale r of he human model are decided by he posiion p = ( x, y) and he size of he head, because he head deecor is much more reliable han oher limb deecor. And hen, he human body will be prediced wih an orienaionα. Afer deermining he relaionships of posiion, scale and orienaion, we can projec he human model ono he image plane. Fig. 4. Parameers ( xyrα,,, ) of upper body projecion. B. Sampling Imporance Resampling (SIR) Paricle Filer Paricle filer, which is based on Bayesian filer wih sequenial Mone Carlo mehod, is a soluion of racking problem based on saisical esimaion. According o Bayesian Theorem, he condiional probabiliy funcion is ( ) ( ) ( ) ( ) p x z p z x p x x p x z dx, () 0: 0: n where x denoe he sae vecor in, and z denoe he m observaion vecor in. In he Bayesian framework, we wan o recursively calculae he belief in he sae x wih all 98

3 pas observaions z 0: = { zi, i =, }, so i is required o p x z. Paricle filer approximaes he Bayesian filer by sequenial Mone Carlo () i () i using a se of weighed paricles x, w. The consruc he poserior ( 0: ) weighs of he samples are normalized, i.e. i he poserior a ime can be approximaed as ( ) () i () i ( ) δ 0: i= { } = = w i () i =, hen p x z w x x, (2) where δ () is he kernel funcion, such as Dirac s dela funcion or Gaussian kernel. One of he general paricle filers is sampling imporance resampling (SIR) paricle filer [], he paricles are generaed from he poserior as a proposal imporance densiy funcion from previous frame. The resampling sep solves he degeneracy problem by he emporal informaion and draws he paricles wih beer observaion inerpreaion bu i causes los of paricles wih he same sae and losses he diversiy of he paricles. This problem is known as impoverishmen phenomenon. To avoid he impoverishmen phenomenon and improve he degeneracy problem, some algorihms employ complex sampling sraegies or specific prior knowledge abou he objecs, such as pariioned sampling [2], layered sampling [3], annealed imporance sampling [4], paricle swarm opimizaion [5], and scaer search echnique [6]. The impoverishmen phenomenon is more obviously in high dimensional sae space. Furhermore, in high dimension sae space, he complexiy and inefficiency grows exponenially. In order o cover he high dimensional sae space, he number of paricles has sufficienly large. In his paper, we combine he SIR paricle filer wih pariioned sampling no only o overcome he degeneracy problem and impoverishmen phenomenon, bu also deal wih he high dimensional sae space. C. Pariioned Sampling Pariioned sampling is a sraegy o alleviae he inense need for a large number of paricles in a high dimensional sae space. This sraegy decomposes he sae space ino wo or more pariions, and sequenially applies appropriae weighed resampling operaion for each pariion. I has been used in he racking of ariculaed objecs [2], fusing he sensors providing informaion abou differen sae space dimensions [7], and human moion capure [8]. Pariioned sampling is suiable for objecs wih hierarchical srucure. [9] proposes a hierarchical par-based deecion, based on he higher reliabiliy of he head-and-orso deecor. This paper follows he idea, so we decompose he high dimensional sae space of he upper body ino wo pars, ha is, for he upper body posure racking, we firs localize he head and orso posiion, nex esimae he arm posure. We don decompose he arm posure ino upper arm and forearm, because he connecion beween upper arm and forearm is igh. Even hough we don model upper arm and forearm separaely, he number of paricles based on our mehod is sill much less han he number of paricles wihou decomposing he high dimensionaliy of he whole upper body. III. UPPER BODY TRACKIG Paricle filer wih he pariioned sampling is used here o solve he upper body racking problem. For saring he sysem, we se an iniial pose as shown in Fig. o consruc he body characerisic of he individual user, such as shoulder widh and arm lengh. While he sysem deecs he human face, i will ry o fi he iniial pose. If he sysem has well fiing, hen i sars he upper body racking procedure. Oherwise, he sysem deermines he person does no wan o inerac wih he robo. Afer seing hese parameers, he 3D human posure which has menioned in Secion II.A can be evaluaed for each paricle. Then measure he weigh of each paricle by likelihood funcion, also called finess and weighing funcion which influences he accuracy and complexiy mos. There are kind of common feaures, such as edge, conour, symmery, exure, color, silhouee, deph, moion. If we can combine several feaures o ge he high accuracy rae, we will loss he efficiency of he sysem. Here, we use edge, color and opical flow informaion o evaluae he likelihood funcion. Knowing he weigh of each paricle, we can evaluae he poserior probabiliy densiy funcion and esimae he arge posiion. In he high dimensional sae space of human model, we use pariioned sampling o draw paricles more efficienly so he paricles number can be reduced. The mos reliable par deecor in human body is he head deecor [9]. We firs decide he sae of head posiion ( x, y ), scale r, and orienaion α of human body. And hen we can use pariioned sampling o generae oher sae ( θr, φr, ρr, γr) for righ arm and ( θl, φl, ρl, γ L) for lef arm. The sae x = ( x, y, r, α),( θ, φ, ρ, γ ),( θ, φ, ρ, γ ), (3) R R R R L L L L can model he upper body posure. By projecing he human model on he image plane, we can ge he image observaion for each sae x and evaluae he weigh of each paricle in full sae space. Bu in cluer environmen, here should conain much edge informaion in he background. The likelihood funcion is no enough o make he accurae decision, even wih he human body consrains. So in he differen siuaion, we use differen mehod o deec he moion body par. We separae he scenario ino saic camera and moion camera which can decide by he camera moion feedback informaion. According o he camera movemen, we use several ways o capure moion region and enhance he edge in he moion region. A. Color Hisogram The color hisogram is compued in YCrCb color space. The reference color model is consruced wih Cr-Cb channel 99

4 o exclude illuminaion influence which is mainly conained in Y channel. The 2D 2 bins reference color hisogram denoed as ref href { href ( i, j) } i=, j= 2 i=, j= h =. (4) The color likelihood funcion is measured wih he Bhaacharyya disance beween he reference hisogram and he hisogram corresponding o each paricle: f i= j= 2 h, h h i, j h i, j, (5) ( x' ) = ( ) ' ( ) i= j= x color ref ref where h x ' is he hisogram corresponding o each paricle, x ' is he subsae abou he measuremen par. For example, if now we esimae he righ arm, hen x ' will conain ( θr, φr, ρr, γ R). The likelihood funcion of color hisogram is defined as he following p ( ) 2 color z x' exp fcolor ( ref, ' ) h h x. (6) B. Enhanced Edge Conour The edge informaion can be uilized o disinguish he oulier of each body par. The conour of head is modeled by ellipse emplae, and he conour of orso and arms are modeled by recangle emplae. Define he conour maching funcion as d i i fconour ( ') = x =, (7) where d i is he disance beween maximum edge and conour emplae, and is he number of sample poins along he conour. The smaller d i represens ha he edge approaches he conour emplae more closely. As a resul, he likelihood of enhanced edge conour is measured by 2 p ( z x conour ') exp fconour ( x '). (8) The edge obained from single image frame is obvious disinguish each human par and background in he simple environmen. However, in he complex environmen, here may exis srong edge in background. The srong edge is easy o arac he concenrae of paricles. In order o enhance he edge around he human, he moion deecion is used. Two kinds of mehods are used for moion deecion wih saic or moion camera. Wih he saic camera, he background image can no sure be aken, so he moving edge is proposed. Two coninuous images are compared o find he differen par. Wih he moion camera, he opical flow can evaluae he differen movemen objec. C. Par Movemen The color and edge only fi he model in 2D, bu he deph informaion is los. To improve he 3D model, he opical flow is a general feaure in a moion video. The feaure poin f ( k ) [20] can exraced from he image and compue he displacemen by racking hese feaure poins a frame. Here, he feaure poins are racked by KLT algorihm [20], which evaluaes he sum-of-squared-difference beween image frames. The movemen of opical flow and he paricle sae variaion for differen body par can be esimae he likelihood funcion. Firs, by he resul of iniializaion, he feaure poins is idenified ino differen body par, such as head, orso, righ arm, and lef arm. ex, we can generae a feaure poin se Bi for each body par: ( ) ( ), ( ) B = f k = x k y k k par i, (9) { ( ) } i where i = H (head), T (orso), LA (lef arm), RA (righ arm) or B (background). By knowing he posiion f ( k ) of each feaure poins, we can approximae he moion model. To simplify our moion model, we do no consider he deph beween objecs in he background and he camera. We only use a simple ranslaion model. Because we have he informaion of which feaure poin belongs o which body par, so we firs evaluae he disance ( k l ) and angle ( k ϕ ) beween feaure poins ( k f ) belong o par i and he cener O, i of he par i wih ( k) ( k) = f O, (0) l, i ( ) ( k) ( k) ϕ =, i f O. () di, x ' beween he feaure poins and he paricle sae of each par: ( k ) ( k) ( k ( ) ) ( k) l x' l ϕ ( x' ) ϕ di, ( x ') = +, (2) 2 ( k) ( k K ) i k i l ϕ where i =H, T, LA and RA. So we can uilize he movemen similariy o generae he likelihood funcion pmovemen ( z x ) for he paricle filer: 2 p ( z x movemen ) exp fmovemen ( x ), (3) in which fovemen ( x' ) = d, i ( x '). (4) 4 i= H, T, LA, RA By using he movemen informaion from feaure poins, we can improve he accuracy of he human posure. And hen, we can evaluae he similariy ( ) D. Overall Likelihood The overall likelihood funcion is he muliply of each cues menioned above C ' ' color Cconour p z x p z x p z x' ( ) color ( ) conour ( ) C ( ') movemen p movemen z x, (5) where he order C color, C conour and C movemen is he weigh of each cue. Usually he weigh are he same, only in special siuaion, he weigh should be modified, such as, C conour should be larger in he complex environmen, and C color should be larger if he background conains he objec wih similar color. 920

5 paricles are employed in he second and hird subspaces. In he following experimenal resuls, we presen some snapshos during he upper body racking in differen scenarios. Fig. 5. The sysem flow char. The overall racking framework is summarized in Fig. 5. The sysem is riggered wih an iniial pose as shown in Fig., and characerisic parameers of he human model are evaluaed from he deeced pose. Pariioned sampling decomposes he sae space ino hree subspaces. The firs space covers he sae describing he head and orso orienaion, and he second and hird subspaces cover he saes for he lef and righ arms. The paricles in he firs pariion are drawn from he likelihood funcion wih he color and conour. The paricles in he second and hird pariions are drawn from he likelihood funcion wih he color, conour and movemen. (a) Resul of iniializaion (b) Frame #36 Fig. 6. The likelihood funcion only wih conour informaion. (a) The likelihood funcion only wih conour informaion. (b) The likelihood funcion wih conour, color, and movemen informaion. Fig. 7. Effec of he moion informaion for he movemen in 3D. IV. HUMA ROBOT ITERACTIO The naural human-robo ineracion should be followed he ease body language for he user in face-o-face ineracion. Using he upper body racking algorihm, he human posure can be analyzed. Only wih he monocular camera, he loss of he deph informaion is difficul o reconsruc he 3D model. However he robo can modify he 3D posure esimaion by change he view angle. The moion informaion changes he human orienaion and he posiion, so he resul can analyze from he well undersanding posiion. A general insrucion is poining gesure as he movemen of he arm owards a poining arge. The robo can esimae he human posure and move o he arge a he same ime by correcing he posure wih 3D human model. Afer saring he racking sysem, he robo can also rack his user and esimae his posure unil he sysem over. Wihou he assumpion for saic camera, he user can inerac wih robo a he comforable posiion, such as si on sofa, or spacious room. This upper body racking algorihm is a echnique which can combine wih oher gesure recogniion and decide he robo behavior. V. EXPERIMETAL RESULT The upper body racking algorihm has been esed on several image sequences, including saic camera and moving camera plaform. In he iniializaion, 4 parameers, which are he lengh and widh for upper arm and forearm, for human model will be deermined. Then he 2 saes in (3) for modeling he 3D human posure will be esimaed hierarchically. When doing he racking wih pariioned sampling, 50 paricles are used in he firs space, and 80 (a) The likelihood funcion only wih conour informaion. (b) The likelihood funcion wih conour, color, and movemen informaion. (c) Edge Image for his scenario. Fig. 8. Effec of he movemen informaion in complex environmen wih noisy edge. Firs, we show he resul only use he conour informaion as described in Secion III.B wih saic camera in he simple environmen in Fig. 6. The resul is good enough for he simple acion wihou orienaion and movemen in 3D. To demonsrae he effec of he movemen of feaure poins wih saic camera in he simple environmen, he resul is shown is Fig. 7. In Fig. 8(a), he join angle beween upper arm and forearm is drif. The join angle is decreasing due o he conour is equally srong in he wrong posure. In fac, he join angle is sill 80 degree which shown in Fig. 8(b). The displacemen obained from feaure poins has anoher benefi. In he es sequence, here have much noise edge in he complex environmen, so he racker wihou feaure poins is easily disurbed by he background. Wih he assisance of he movemen informaion from feaure poins, he proposed racker can successfully esimae he arm posiion. The human size can no evaluae from he disance beween he human o camera, bu can be modified by he scale of he 92

6 head, because he size of every par is in proporion o he face size. As shown in Fig. 9, even he human close or move away, he racker can capure he human wih righ size. Fig. 9. The face scale influence. The human posure is projeced on he image plane. There have some similar image observaion by differen posure according o he projecion, even he movemen of feaure poins can no solve he projecion problem. Here, we can move he camera plaform o a beer posiion, and hen correc he human posure. For example, when he shoulder angle θ < 60, he racker can fi he righ join posiion, bu over his range, even hough he racker sill mach wih he arm, he join posiion drifed away. By moving he camera plaform, he join posiion can be modified o he righ posiion. Figure 0 presens his siuaion, and he proposed racker gradually obain he correc posure of righ arm. Fig. 0. Moving camera plaform o modified he human posure. VI. COCLUSIO This paper presened an upper body racking algorihm based on paricle filer wih pariioned sampling. The model uses color, edge and movemen informaion. Unlike oher racking algorihm which requires he background informaion o cu a human silhouee, we do no need a saic background. In order o apply he algorihm o human-machine ineracion, our approach can handle he siuaion wih moving camera plaform and uilize he differen view angle o correc he 3D human posure. The compuaional ime for he overall sysem now is abou 5 fps, which achieves he near real-ime performance. In his paper, he human model only limied he join angle, so someimes i may generae impossible posure. In he fuure, we plan o combine learning based human model, such as rained by SVM (suppor vecor machine) or RVM (relevance vecor machine), o prove he human posure esimae. Moreover, we will examine and analysis our sysem in more complicaed scenarios of human-robo ineracion and aim o reduce he overall compuaional ime. ACKOWLEDGMET This work was suppored by he aional Science Council of he Republic of China under Gran SC E REFERECES [] J. K. Aggarwal and Q. Cai, "Human moion analysis: a review," in onrigid and Ariculaed Moion Workshop, 997. Proceedings., IEEE, 997, pp [2] D. M. Gavrila, "The visual analysis of human movemen: A survey," Compuer Vision and Image Undersanding, vol. 73, pp , 999. [3] T. B. Moeslund and E. Granum, "A survey of compuer vision-based human moion capure," Compuer Vision and Image Undersanding, vol. 8, pp , 200. [4] T. B. Moeslund, A. Hilon, and V. Krüger, "A survey of advances in vision-based human moion capure and analysis," Compuer Vision and Image Undersanding, vol. 04, pp , [5] J. J. Wang and S. Singh, "Video analysis of human dynamics--a survey," Real-Time Imaging, vol. 9, pp , [6] J. Lee, J. Chai, P. S. A. Reisma, J. K. Hodgins, and. S. Pollard, "Ineracive conrol of avaars animaed wih human moion daa," ACM Transacions on Graphics (TOG) archive, vol. 2, pp , [7] K. 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