Inference of Human Postures by Classification of 3D Human Body Shape

Transcription

1 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 Inference of Human Postures by Classfcaton of 3D Human Body Shape Isaac COHEN, Hongxa LI Insttute for Robotcs and Intellgent Systems Integrated Meda Systems Center Unversty of Southern Calforna Abstract In ths paper we descrbe an approach for nferrng the body posture usng a 3D vsual-hull constructed from a set of slhouettes. We ntroduce an appearance-based, vew-ndependent, 3D shape descrpton for classfyng and dentfyng human posture usng a support vector machne. The proposed global shape descrpton s nvarant to rotaton, scale and translaton and vares contnuously wth 3D shape varatons. Ths shape representaton s used for tranng a support vector machne allowng the characterzaton of human body postures from the computed vsual hull. The man advantage of the shape descrpton s ts ablty to capture human shape varaton allowng the dentfcaton of body postures across multple people. The proposed method s llustrated on a set of vdeo streams of body postures captured by four synchronous cameras. 1. Introducton Multmodal nteracton systems represent a consderable shft from classcal wndows, cons, menus and pontng (WIMP) nterfaces. Gesture and speech represent the man component of such nterface as they correspond to the foundaton of natural human communcaton. Whle speech recognton systems are commercally avalable, gesture recognton s stll n ts nfancy. Ths s partally due to the fact that speech modalty s lnear and very structured whle gesture s a spatal modalty that s stll challengng to capture and nterpret. Human body moton trackng and analyss has receved a sgnfcant amount of attenton n the computer vson research communty n the past decade. Ths has been motvated by the ambtous goal of achevng a vsonbased perceptual user nterface n whch the state and the acton of the user(s) are automatcally nferred from a set of vdeo cameras. The obectve s to extend the current mouse-keyboard nteracton technques n order to allow the user to behave naturally n an mmersed envronment, as the system perceves and responds approprately to user actons. Understandng human acton n an envronment s a challengng task as t nvolves dfferent granularty n ts analyss and descrpton accordng to the targeted applcaton. For example, descrbng a human actvty n term of ts traectory consttutes a frst level of representaton, whch may be satsfactory for survellance applcatons but remans qute nsuffcent for understandng human gesture n an nteractve envronment. Indeed, n such stuatons rcher descrptons are requred n order to understand the human actvty and recognze the performed gestures. In ths paper we wll focus on the capture and the descrpton of human body 3D shape for the dentfcaton of human body posture. We beleve that posture recognton s a frst step towards gesture recognton. Indeed gestures can be decomposed nto a set of basc postures that characterze temporal evoluton of the performed gesture Prevous Work Varous methods have been proposed for the estmaton and analyss of full-body structure (see [8] and references theren). The obectve s to develop real-tme nteractve systems wth more sophstcated 2D and 3D trackng and representatons [11][17]. Understandng the human moton from a monocular mage sequence s challengng snce only the 2D proecton of these arbtrary motons s captured. Recently several researchers focused on the nference of 3D body model from a monocular camera usng a human body model [6][15] or temporal templates [25]. The man drawback of these technques s that roughly one thrd of the degrees of freedom of the human model are nearly unobservable due to moton ambgutes and self-occluson. Multple vews are therefore requred to dsambguate or dentfy the human moton. Several approaches have been proposed for estmatng human postures n the 3D case. These approaches rely on two to an array of cameras to capture the human shape and moton [7][12] or use 3D body scanners [19]. The body postures are then characterzed through the use of shape descrptors or by characterzng body onts confguratons. Whle the use of an artculated body model provdes accurate measurements of body onts confguratons, t requres ntensve computng and state of the art technques stll lack robustness and accuracy for rapd hand moton Outlne of the Proposed Approach We present n ths paper a shape posture dentfcaton technque based on the classfcaton of the human body

2 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 shape. The human body 3D shape nferred from a set of slhouettes and the correspondng vsual hull s characterzed by a shape descrpton defned by a dstrbuton. We ntroduce n ths paper a 3D shape descrpton allowng dentfyng varous body postures. The shape descrpton accounts for varablty n people s body proportons and provdes nvarance to translaton, rotaton and scale. Contnuty propertes are also satsfed provdng a robust shape descrptor that exhbts localzed varaton of the dstrbuton for localzed 3D shape varaton. Moreover, the 3D shape descrptor we propose can selectvely encode prvleged axs of symmetry or desred rotaton nvarance. These propertes are mportant for human posture dentfcaton, snce the human body possesses a symmetry axs. Identfyng body postures from ts 3D shape s challengng as the 3D descrpton of the shape has to account for shape varablty n characterzng a posture. Indeed, several people wll perform smlar posture dfferently and therefore dentfyng a posture from the 2D/3D shape descrptons wll requre a learnng step. We present an appearance-based, learnng formalsm that s vew pont ndependent and uses a 3D shape descrptor of the vsual-hull for classfyng and dentfyng human posture. The proposed method does not requre an artculated body model ftted onto the reconstructed 3D geometry of the human body. In fact, t complements the artculated body model snce we can defne a mappng between the observed shape and the learned descrptons for nferrng the parameters of the artculated body model. In the followng secton we wll present the shape descrptor consdered and the learnng algorthm based on Support Vector Machne (SVM).Our approach s based on the ntegraton of 2D slhouettes captured by two or more cameras and the descrpton of the human body shape usng a 3D shape descrptor generated from the vsual hull of the human body. An overvew of the proposed approach s gven n Fgure 1. Camera 1 Slhouette 1 Camera 2 Slhouette 2 3D Vsual Hull Shape Descrptor SVM Body Postures Recognton Fgure 1: Overvew of the proposed approach. Camera N Slhouette N The paper s organzed as follows; secton 2 descrbes the 2D shape descrptor and ts generalzaton to 3D. Secton 3 presents the body postures classfcaton and dentfcaton based on SVM approach. Secton 4 exposes the expermental sde of ths work. It descrbes the defnton of the model postures and llustrates the classfcaton results. The paper s concluded by dscussng the results, potental mprovements and future work. 2. Human Body Shape Descrpton Shape descrptors have been well studed n varous felds as they are used for determnng the smlarty between two shapes. The derved descrptors can be classfed n terms of the shapes they characterze.e. 2D contours, 3D surfaces, 3D volumes... For example, bendng energy functons, spn mages [1], shape context descrptors [1]. These descrptors were manly used for shape matchng and therefore focused on characterzng the local propertes of the shape. Global models, assume a descrpton of the obects nto a set of features or parts segment. Common descrptons rely on parametrc models [16], deformable regons [2][3], shock graphs [14] or wavelet decomposton [19] and sphercal harmoncs [22]. Shape smlarty s then measured by comparng locaton of features and ther spatal dstrbutons. The performances of these approaches depend on the dffcult task of segmentng the shape nto ts correspondng parts. These technques perform well n the case of shapes of fxed confguratons and are not sutable for modelng varablty n the observed shapes such as the 3D vsual hull of a gesturng person. Fnally, a thrd descrpton approach s based on modelng the geometrc dstrbuton of the shape propertes such as hstograms of angles [9] and dstances between par of arbtrarly sampled ponts [23],... These descrptons are hstogram based and do not perform well as the localzaton of the features s lost n the statstcal representaton used. We present a statstcal shape descrpton model that preserves the localzaton of the geometrc features consdered. Ths global representaton allows a robust descrpton of shape that accommodates for varaton of the shape. Indeed, as one would expect, small shape varaton should nduce a small change n the obect descrpton. Moreover, ths varaton s localzed and does not nterfere wth the global representaton of the obect. These propertes of the proposed shape descrpton are crucal for effcently representng the human shape and ts varatons. The proposed shape descrptor descrbes a 3D polygon wth regard to a reference shape. The reference shape consdered characterzes the propertes of the descrptor. Usng a crcle as reference for a 2D shape wll guarantee rotaton nvarance and smlarly a sphere for a 3D shape. Ths allows to selectvely choosng the propertes of the descrptor accordng to the desred

3 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 applcaton. The use of a cylnder as reference shape wll guarantee rotaton nvarance only along ts man axs and wll enforce an axal symmetry. These propertes are partcularly nterestng for human body shape descrptors. Indeed a human body shape has such propertes. Our shape descrpton approach s based on the 3D vsual hull reconstructed from the detected 2D slhouettes. Let s frst descrbe the proposed descrpton scheme n 2D and then generalze t to 3D vsual hulls D Shape Descrpton Gven a 2D slhouette of an obect we compute a reference crcle C R defned by the centrod of the slhouette and ts man axs. Ths crcle s unformly sampled nto a set of control ponts. We then consder a polar encodng of the proecton of the slhouette onto the set of ponts P }. For every pont Q of the slhouette { we compute the polar encodng of P Q, defned byq P r cos θ, sn θ. For each control pont we = ( ) nfer a bnned polar dstrbuton where n each bn r k, θ l. We then store the number of slhouette ponts ( k, l) proected onto that bn. In Fgure 2 we show the N contrbuton of a sngle pont P P ( ) to the shape descrptor. The geometry of the bn s depcted n hashed green lnes. The 2D shape descrpton s obtaned by addng and normalzng the set of descrptons derved from each pont : P P N ( k, l) N( k, l) = Max( N ( k, l)), k l sgnature functon, n polar coordnates was centered on the centrod and rendered n Cartesan coordnates. Note that ths only a vsualzaton of the representaton map. The real shape sgnature s n the polar coordnate system. (a) (b) (c) Fgure 3: Example of a 2D global shape descrptor of a walkng person. (a) the 2D slhouette. (b) 2D shape descrptor reproected on the slhouette. (c) 2D shape descrptor n polar coordnates D Shape Descrpton The 2D shape descrpton we descrbed prevously can be generalzed to 3D wthout losng ts propertes (e. scale, rotaton and translaton nvarance). In the 3D case, nstead of consderng a sngle human body slhouette, a set of slhouettes are acqured synchronously. The 3D shape descrpton s derved n two steps: 1. Construct the trangulated vsual hull surface from the set of slhouettes. 2. Derve the 3D shape descrpton of the human body based on the trangulated surface representaton of the vsual hull D human body vsual-hull reconstructon Integratng multple slhouettes acqured smultaneously from dfferent vew ponts allows generatng a 3D vsualhull of the human body. The vsual-hull of an obect s the closest approxmaton of the 3D obect whch can be obtaned from the detected 2D slhouettes [13]. Assume that the person s vewed from a set of cameras. Each slhouette defnes a cone characterzed by all the rays from the camera orgn to the ponts on the slhouette. The ntersecton of the cones generated by the multple cameras defnes the vsual hull of the obect. Fgure 2: Illustraton of the global shape descrptor of a 2D slhouette The shape descrpton derved here s ndependent of the scale of the slhouette as the descrpton s normalzed by the radus of the consdered reference crcle C R. The translaton nvarance s obvous whle the rotaton nvarance s also guaranteed. Indeed, rotatng a slhouette n the mage plane mples a cyclc permutaton of the ponts leavng the sgnature unchanged. In Fgure 3 we P show an example of such representaton where the Fgure 4: The body slhouette detecton. The blue regons correspond to the detected and removed shadow, green lne corresponds to the detected edges and the pnk regon represents the fnal human slhouette

4 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 Human slhouettes are extracted by calculatng a Gaussan-based model of the statc scene and then comparng each new observaton to the background model dstrbuton. Such approach detects pxels where a moton was observed, as well as the shadows and reflectons. However, n ndoor envronments the shadows cast by a dffuse lght do not have strong boundares. Therefore, combnng edge propertes to color varatons allows us to remove the pxels belongng to cast shadows and segment accurately the foreground pxels. In Fgure 4 we show an example of such detecton approach. The ntegraton of the detected slhouettes provdes a 3D representaton of the human body. A polyhedral representaton of the detected slhouettes and ther ntegraton wll provde a polyhedral approxmaton of the vsual hull. The vsual hull s computed usng a polyhedral representaton for the vsual hull drectly from the detected slhouettes [2]. If the number of slhouettes consdered s large the vsual-hull provdes a good approxmaton of the 3D shape. Ths polygonal approxmaton of the shape can be computed n real tme and also converted nto a trangular descrpton usable by a trangular processng framework. In Fgure 5 we show an example of a 3D vsual hull computed from three vews. Fgure 5: 3D vsual-hull reconstructed from the 3 slhouettes shown on the left D Shape Descrpton The generalzaton of the 2D shape descrptor to 3D s performed by defnng a reference shape contanng the vsual hull surface and measurng the contrbuton of each trangle of the vsual hull to the shape descrpton nferred from a set of ponts lyng on the surface of reference. The selecton of the surface of reference allows choosng the propertes of the descrptor accordng to the desred applcaton. The use of a cylnder as reference shape wll guarantee rotaton nvarance only along ts man axs and wll enforce an axal symmetry whle a sphere wll provde 3D rotaton nvarance and pont symmetry. These propertes are partcularly nterestng as they allow dervng an applcaton dependent 3D shape descrpton. For human body posture recognton, the use of a cylnder as reference shape enforces body axal symmetry whle for a generc 3D obect descrpton, a sphere wll be more approprate. The computaton of the 3D shape descrpton s smlar to the 2D case. The man dfference between the 2D and 3D s the representaton of the human body. In 2D, t s represented by all the ponts on the slhouette. But n 3D, the representaton s based on the 3D trangular vsual hull surface constructed from a set of slhouettes. In order to use the shape descrpton method we proposed for the 2D case, we need to sample the vsual hull surface nto a set of ponts, whch are dense enough to contan all the nformaton of the vsual hull. Because the sze of the trangular vsual hull surface s not unform and depends on the polygonal approxmaton of the slhouettes, we need more sample ponts on the surface other than the trangle vertces. One could refne the trangular descrpton by subdvdng the 3D vsual hull. Ths wll ncrease dramatcally the algorthmc complexty of the algorthm and create redundant mesh descrpton. Instead, we have chosen to sample unformly the trangles of the vsual hull and encode the relatonshps between the sampled ponts and the reference surface consdered. Q, =.. m Gven a set of ponts { } correspondng to a samplng of the vsual hull and a set of P, =.. n sampled unformly on the control ponts { } surface of the reference shape control pont P proectons of the set of ponts. We compute, for each, the dstrbuton of 3D sphercal. The sphercal encodng of P Q, defned byq P = ( r, θ, ϕ ). For each pont we then nfer a bnned sphercal dstrbuton where n P each bn S R Q ( r,θ,ϕ ) we store the number of 3D ponts proected onto that bn. The 3D shape descrpton s obtaned by addng and normalzng the set of descrptons derved from each control pont. In Fgure 6 we show the sphercal shape dstrbuton obtaned by encodng a 3D vsual hull wth regard to a cylnder and a sphere. The shape dstrbuton vares consderably as dfferent shape propertes are captured by the two reference surfaces Smlarty Propertes The purpose of defnng a shape descrpton s the ablty to characterze surfaces local and global smlartes, as well as comparng varous 3D surfaces. The shape descrptor of a surface s defned by a dstrbuton n the sphercal coordnate space. Comparng shapes s therefore reduced to comparng the correspondng dstrbutons. P Q

5 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 We have used the above dstance functon to show that the global descrptor s nvarant to scale, translaton, and rotaton. Also small varatons of the shape of the human body create small localzed varatons of the shape descrpton (contnuty). The robustness of the descrpton wth regard to nose n the computed vsual hull s llustrated n Fgure 8. We have added a Gaussan nose to the vertces of the trangles of the vsual hull depcted n Fgure 6.a and measured the smlarty of the shapes. The nosy vsual hull and the correspondng shape dstrbuton are llustrated n Fgure 8. A comparson of the smlarty between the orgnal vsual hull and ts nosy counterpart yelded a dstance value less than 1 postures consdered. (a) 2 for the 12 body (b) Fgure 6: 3D vsual hull and the shape dstrbuton of the surface usng a cylnder (a) and a sphere (b) as reference surface. Measurng the smlarty among dstrbuton can be performed by varous methods defnng dstance functons. We chose to characterze smlarty between shape dstrbutons by measurng the relatve entropy of the dstrbutons. Gven two dstrbutons f and g, the relatve entropy, also called the Kullback-Lebler dstance, s defned by: d( f, g) = f log 2 g Ths new dstance functon llustrated n Fgure 7 s used n the remanng of the paper for measurng the smlarty between two surfaces. (a) (b) (c) (d) f g Even though ths dstance does not satsfy the trangle nequalty (t s not a true metrc) t has many propertes of dstance functons and s equals to zero only f f = g. In order to guarantee symmetry of the smlarty measure between two dstrbutons we have derved the followng dstance: 1 (d ( f, g ) + d ( g, f ) ) = 1 ( f g ) log f D( f,g) = 2 (a) (b) Fgure 7: Graphs of the dstance functons consdered. (a) Kullback-Lebler dstance and (b) ts symmetrc counterpart. Fgure 8: Stablty of the shape descrptor wth regard to nosy nformaton. (a) nosy vsual hull ( σ = 2 ) and ts shape descrptor (b). (c) and (d) dsplay the polar sphercal dstrbutons for a selected radus (r=.1) of the nosy vsual hull depcted n (a) and the orgnal one llustrated n Fgure 6.a. In Fgture.9, we show the 3D descrptor for another body posture. It depcts the varablty of the shape sgnature accordng to the selected reference shape and ts ablty to capture body shape propertes. In the remanng of the paper, we have consdered the cylnder as a reference shape for dervng the shape descrptors of the computed vsual hull. Ths permts to enforce the axal symmetry of the body shape.

6 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 (a) Orgnal mages (b) (c) Fgure 9: Example of shape descrptors derved usng a cylnder and a sphere as reference shape. In (b) and (c) we dsplay the vsual hull and the sphercal shape descrptor vewed from above and the sde. 3. Human Body Posture Inference Dervng the human posture from ts slhouettes n 2D or from the reconstructed shape n 3D s a challengng task as t requres takng nto account posture varablty across people. A method commonly used reles on the artculated body model n order to nfer the human posture. The recovery of an artculated body model stll requres the nterpretaton of the 45plus-degree of freedom n order to nfer the human posture. Ths nterpretaton has to take nto account posture varablty and errors n the estmaton of the artculated model n order to perform an effcent analyss of the 45plus-D parameter space. In the followng sectons we show that the global 3D shape descrptor ntroduced n ths paper can be used for human body posture nference. We use a Support Vector Machne formalsm [18] to tran and classfy the set of heterogeneous nformaton provded by the 3D shapebased descrptor. The man advantage of usng a SVM s ts ablty to compress the nformaton contaned n the tranng set, snce only support vectors are requred for the classfcaton. Ths allows us to reach near real-tme performances SVM-based Classfcaton The man ssues n usng a machne learnng approach are the selecton of the features used as tranng data set and the choce of the data set for tranng the model. Whle an artculated body model provdes a natural set of features (onts and lmbs) to consder for tranng purposes, t s tme consumng and t s dffcult to acqure the 45plus degrees of freedom of the selected model. Conversely, 3D shape based descrptors are very easy to collect but a correct representaton of the shape has to be selected n order to be sgnfcant for learnng. The problem we are addressng here s the defnton of a decson functon that from a set of observatons x X = { x, = KN} and the correspondng labels y Y = { y, = KM} wll make accurate classfcaton of unseen values of x. A very successful approach for solvng ths supervsed learnng problem s the support vector machne (SVM) [18]. In ths work we are nterested n a classfcaton of the observed human postures; therefore the set of avalable labels s lmted to Y = { 1,1} representng respectvely non-posture and posture descrptons. The decson functon s defned by the SVM s: l ( ) = 1 f x sgn α y K( x, x) + b = where the coeffcents are obtaned by maxmzng the functonal: α l 1 l 1 1 ( α ) = α α α y y K( x x ) W, = 2, = under the constrants: l 1 and α y = α. = The coeffcents α defne a maxmal margn hyper-plan n the hgh dmensonal feature space where the data are mapped through the non lnear functon φ such that ( x ) φ( x ) = K( x, x ) φ. Varous kernels K are commonly used (lnear, exponental, polynomal...) we wll use a lnear kernel K usng therefore a lnear mappng between the feature space (posture we defned) and the representaton space (shape descrpton) Tranng and Classfcaton Selecton of Body Postures We have defned a set of 12 postures that need to be dentfed by the system. The selected postures are shown n Fgure 1. The selected postures focus on hand gestures and were chosen n order to buld a representatve set of postures lkely to be observed as people nteract wth a system usng hand gestures.

7 IEEE Internatonal Workshop on Analyss and Modelng of Faces and Gestures, ICCV 23 Fgure 1: The set of 12 postures we defned n our system Posture tranng and classfcaton The SVM s traned usng 3D shape descrptors defned n the prevous secton. The shape descrptor s nferred from the 3D vsual hull obtaned by ntegratng mult-vew slhouettes acqured by 4 synchronous cameras. The shape descrptor s presented by a vector: S= {(ndex of the bn, densty of ponts n the bn)}. The vsual-hull correspondng to the detected slhouettes and ther shape descrptors are computed at a frame rate of one frame per second. The system was traned on the chosen 12 postures by consderng approxmately 2 samples per posture. 4. Expermental Results We have used the proposed 3D shape descrpton technque for dentfyng user s postures whle performng specfc gestures. The expermental settngs contans four synchronzed cameras allowng to extract n real tme the human body slhouettes and nfer the 3D vsual hull at one frame per second. In Fgure 11 we show the output of the system. In our experments, the number of control ponts used to generate the shape descrptor s Less control ponts cannot accommodate all rotaton varaton, whle more control ponts do not sgnfcantly mprove the performances but requre more computng. The selecton number of bns depends on the complexty of the predefned postures. In our case we have used the followng: 1(r) 1(θ) 1(ψ). Fgure 11: Illustraton of the system s output. The thumbnal of the recognzed posture s hghlghted. The detected slhouettes and the correspondng vsual hull are dsplayed, whle at the bottom, the system hghlghts the mage thumbnal correspondng to the recognzed posture. The performed postures are recognzed by the system even though the vsual hull s the detected slhouettes are corrupted by reflectons on the wall. Moreover, the poston and orentaton of the consdered persons are dfferent from the one used for tranng the classfer. These results show the capablty of the system to handle people that were not used for tranng and handle varatons n body proportons and the person s pose whle performng a specfc gesture Identfcaton Rate We have evaluated the performances of the system for several people. None of the persons tested were consdered n the tranng phase. In fact the SVM was traned usng postures from one sngle person. Therefore, we expect the recognton rates to be mproved as we broaden the set of people consdered for tranng the SVM-based classfer. In table 1 we show the recognton rates obtaned on 2 vdeo sequences (contanng each about 2 frames) of 4 dfferent persons. For each posture the rates dsplayed correspond to averages of the obtaned recognton rates. Person Posture Table 1. Identfcaton rates of the 12 postures for 4 dfferent persons. Only person 1 was used for tranng the SVM. 5. Concluson Identfyng user postures as a frst step towards gesture recognton s a challengng task. The challenge here was to defne a 3D shape descrpton that allows a robust characterzaton across users wthout requrng a specfc tranng for each person. The presented expermental results llustrated the ablty of the system to recognze a varety of human body posture. We have started

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