Detection and segmentation of moving objects in highly dynamic scenes

Transcription

1 Deecion and segmenaion of moving objecs in highly dynamic scenes Aurélie Bugeau Parick Pérez INRIA, Cenre Rennes - Breagne Alanique Universié de Rennes, Campus de Beaulieu, Rennes Cedex, France {aurelie.bugeau,perez}@irisa.fr Absrac Deecing and segmening moving objecs in dynamic scenes is a hard bu essenial ask in a number of applicaions such as surveillance. Mos exising mehods only give good resuls in he case of persisen or slowly changing background, or if boh he objecs and he background are rigid. In his paper, we propose a new mehod for direc deecion and segmenaion of foreground moving objecs in he absence of such consrains. Firs, groups of pixels having similar moion and phoomeric feaures are exraced. For his firs sep only a sub-grid of image pixels is used o reduce compuaional cos and improve robusness o noise. We inroduce he use of p-value o validae opical flow esimaes and of auomaic bandwidh selecion in he mean shif clusering algorihm. In a second sage, segmenaion of he objec associaed o a given cluser is performed in a MAP/MRF framework. Our mehod is able o handle moving camera and several differen moions in he background. Experimens on challenging sequences show he performance of he proposed mehod and is uiliy for video analysis in complex scenes.. Inroducion Deecion of moving objecs in sequences is an essenial sep for video analysis. I is a difficul ask in he presence of a dynamic background. Differen kinds of mehods exis o solve he problem of moion deecion and moion segmenaion. One of hem is background modeling and subracion, which is a preliminary sep o moving objec deecion and subsequen processing is necessary o ge he masks of moving objecs. Firs works were based on adjacen frames difference [2]. However, his simple mehod is unsuiable for real world siuaions and saisical mehods were inroduced o model he background. Background modeling mehods can be classified as predicive or non predicive mehods. Non-predicive mehods build a probabiliy densiy funcion of he inensiy a an individual pixel [8] [0]. Non parameric approaches are more suied when he densiy funcion becomes complex [7]. Unil recenly, mos mehods were based on phoomeric properies. In [5], Mial and Paragios presen a non parameric algorihm ha combines color and flow feaures, and inroduce a variable bandwidh kernel. Predicive mehods use a dynamical model o predic he value of a pixel from previous observaions [25]. All hese pixel-wise approaches allow an accurae deecion of moving objecs bu are memory and possibly compuaionally expensive. Also, hey can be sensiive o noise and hey don ake ino accoun spaial correlaion. For hese reasons, spaial consisency can be added as in [9], where a MAP-MRF modeling of boh foreground and background is used o deec moving objecs. This mehod has been exended o novely deecion in [4]. Feaure based models also exis for background modeling. For example, in [26], he background is modeled only on corners, and moving objecs are hen found by he clusering of foreground feaures rajecories. For numerous oudoor sequences, he changes in he background appear suddenly and, in case of grayscale videos, he objecs may have inensiy values close o he ones of he background. Hence, background modeling is difficul and ofen no sufficien. Anoher approach o deec moving objecs is o exrac groups of salien moion by accumulaing flows consisen in erms of direcion over successive frames [23] [20]. Moion segmenaion can also be seen as he problem of fiing a collecion of moion models o he image daa. These layered approaches ofen use EM algorihm [22] or more recenly graph cus [24] o exrac layers. The problem can also be cas in erms of muli-body facorizaion, and many papers can be found on his subjec when he scene is saic. In [2], i was adaped for boh saic and dynamic scenes. Recenly, in [7], an incremenal approach o layer exracion has been inroduced. Feaure poins are deeced, racked and hen merged ino groups based on heir moion. Objecs are deeced incremenally when enough evidence can disinguish hem from heir background.

2 In his paper, we are ineresed in challenging sequences conaining complex moions, possibly wih high ampliude, and sudden changes in he background. For example, in he conex of driver surveillance, he moions visible hrough he windows are ofen hard o characerize. The background is composed of boh he passenger comparmen and wha is behind he windows. Furhermore, conras beween background and ineresing objecs (face, hands) can be low. Also, he sequences we consider can be sho by a moving camera. Our work does no aim a modeling he background or a finding every layer bu only a deecing moving foreground objecs. We define hese objecs as groups of pixels ha are salien for boh moion and color. Our algorihm can be divided in four main seps. Firs, he camera moion is compued and he images recified (secion 2). All pixels whose moion is close o he camera moion are lef apar for he wo nex seps. In order o reduce he compuaional cos and o be more robus o noise, we resric momenarily he analysis o a subgrid of moving pixels, i.e. no belonging o camera moion (secion 3). A descripor is defined o characerize hem. They are hen merged ino clusers consisen for boh color and moion (secion 4). From he clusers, he complee pixel-wise segmenaion of moving objecs is found using a MAP-MRF framework (secion 5). Finally, secion 6 presens some experimenal resuls. 2. Sensor moion Mos of he es sequences we are working on have been aken by a moving handheld camera. We assume ha he apparen moion induced by he physical moion of he camera is dominan in he image and is well approximaed by an denoes he grayscale he color image and P he se of pixels. The displaced frame difference beween is given by: affine moion field. In his paper, I (g) image a ime, I (c) in he image I (g) wo consecuive frames I (g) + and I(g) D (p) = I (g) (g) + (p + w(p)) I (p) + ζ, () where p is a pixel (p P), w (p) he associaed flow vecor and ζ a global inensiy shif o accoun for global illuminaion changes. As in [6], he esimaion of he parameers defining moion field w and global shif ζ is done using an M-esimaor. The weigh map of he M-esimaor is denoed as W (W (p) [0, ]). The final map indicaes if a pixel paricipaes o he robus moion esimaion (W (p) close o ) or is more considered as an oulier (W (p) close o 0). A simple pixel-wise moion deecor can be buil using his map. A pixel is considered as moving a ime if i is an oulier o he dominan moion a imes and : j if W(p + w (p)) + W (p) = 0 M (p) = (2) 0 else. If, for a pixel p, M (p) = 0, i is considered as a moionless pixel. In he sequel, Ĩ(g) + will denoe back-warped images: Ĩ (g) (g) + (p) = I + (p + w (p)) + ζ. 3. Selecion and descripion of poins The goal of he algorihm is o build and segmen groups of pixels consisen boh for moion and for some phoomeric or colorimeric feaures. These groups mus correspond o ineresing moving objecs. Processing is only done on a subse of moving poins and heir neighborhoods. This secion presens he definiion of his subse of poins and he poin descripion used o perform clusering. 3.. Selecion In [26], he auhors have chosen o use corners, deeced wih he Harris corner deecor. The auhors jusify he use of corners by claiming ha a moving objec conains a large number of corners. In our experimens, we have observed ha he number of corners belonging o a moving objec can be much lower han he number of corners belonging o he background. Besides, if variaions in he background are fas and if parallax changes, he number of corners and heir neighborhood can be signicaively differen from one frame o he oher. Finally, corner deecion adds one sage of calculaion and requires wo hresholds. As no a priori is assumed abou he shape and exure of objecs, we have chosen o use poins of arbirary ype. Hence, we only use a grid of poins regularly spread on he image. As he purpose is o deec moving objecs, he simple pixel-wise moion deecor from secion 2 is used o resric his sep o he grid subse: G = {p = ( k.w N, l.h ), k = 0 N, l = 0 N M(p) = }, N (3) where w and h are he dimensions of he image and N 2 he size of he grid before pruning. The value of he parameer N is imporan. I conrols he balance beween compuaional cos (regional mehods) and accuracy (local mehods). Nex sep of he algorihm can become compuaionally expensive if he number of poins of he grid is oo large. An imporan hing o noe is ha N may depend on he number m of moving pixels in he image, m = P p P M(p). To limi he compuaional cos for clusers creaion, we fix he number of poins n (000 in our experimens) ha will be kep in furher seps of he algorihm. The size N of he grid is hen se as N = p w h n/m Descripion Now ha he poins are chosen, he feaures ha will be used o creae clusers corresponding o objecs need o be defined. I is necessary o chose only few discriminan feaures. An objec is defined as a moving and compac area over which he values of displacemen and phoomery are nearly consans. Color is no sufficien because he conras beween an objec and he background can be small,

3 as is flow in case of similar moion beween an objec and he background. Hence he descripor is formed by hree differen groups of feaures. The firs group is composed of he coordinaes of he poin. The second group conains is moion, and he las one conains discriminan phoomeric feaures Moion feaures As we ry o deec moving objecs, an essenial feaure is he displacemen of he seleced poins. I is compued using an opical flow echnique robus o local linear illuminaion changes. We used Lucas and Kanade algorihm [3], wih an incremenal muliscale implemenaion. A parameer a ha accouns for local illuminaion changes has been added. The flow (d x, d y ) a each paricular poin p = (x, y) of he grid is hen obained by solving: argmin a,dx,d y (x,y ) V(p) (aĩ(g) + (x +d x, y +d y) I (g) (x, y )) 2 (4) where V(p) is he neighborhood of p. As i is well known, Lucas and Kanade algorihm has some drawbacks: he brighness consancy is no saisfied and here is no spaial consisency. We could have used Horn and Schunk algorihm [] ha adds a smoohness erm o regularize over he whole image or he robus esimaion of Black and Anandan [] o ge a beer esimaion. However hese algorihms are more expensive and we do no aim a having a perfec esimaion over he all image. To validae values of displacemen, a comparison is done beween he neighborhood of pixel p = (x, y) in image a ime (daa sample ), and he neighborhood of poin p = (x + d x, y + d y ) a ime + (daa sample Y ). The linear relaionship beween inensiy values of and Y is esimaed by compuing he normalized cross correlaion r. Unforunaely, he correlaion does no ake ino accoun he individual disribuions of and Y. Hence i is a poor saisics for deciding wheher or no wo disribuions are really correlaed. Saisical ess exis o assess his correlaion. One of such ess is based on so-called p-value. The p-value is he probabiliy ha he resuls have been obained by chance alone. Here he null hypohesis assers ha he wo disribuions are uncorrelaed. If one wans o limi o 5% he risk ha a false posiive error has occurred, hen daa are assumed correlaed if he p-value is lower han If no, he moion vecor a poin p is considered as a non valid and will no be used in nex seps of he algorihm. Finally, a new grid G = {p = ( k.w N, l.h N ) M(p) = & pvalue(p, p ) < 0.05} (5) is obained wih a flow vecor F (p) associaed o each of is poin p. The size of he grid G will be denoed as M = G Phoomeric feaures To be robus o noise, he phoomeric feaures are compued over he neighborhood of each poin of he grid defined in previous subsecion. We observed ha he hree RGB color channels do no give he bes represenaion of images. In fac mos of our es sequences conain human skin, which has a specific signaure in he space of chrominance. Hence, i is ineresing o use insead a color sysem represening he chrominance, e.g., he sysem YUV. This choice proved appropriae for various ypes of sequences. To include some simple emporal consisency, we add image + chrominance values of he corresponding poin. Finally, he descripor a each individual valid poin indexed by i (i =... M) of he grid is: x (i) = {x (i), x(i) where x (i) = {x, y}, x (i) 2 = {d x, d y}, x (i) 3 = {Y (x, y), U (x, y), V (x, y), 2, x(i) 3 }, (6) Y +(x, y ), U +(x, y ), V +(x, y )}, wih (x, y ) = (x + d x, y + d y), and. denoes he mean over he neighborhood. 4. Grouping poins Now ha a grid of valid poins has been chosen and described, we address he problem of grouping he poins ino clusers. 4.. Mean shif for mixed feaure spaces An appealing echnique o exrac he clusers is he Mean Shif algorihm, which does no require o fix he (maximum) number of clusers. On he oher hand he kernel bandwidh and shape for each dimension has o be chosen or esimaed. Mean shif is an ieraive gradien ascen mehod used o locae he densiy modes of a cloud of poins, i.e. he local maxima of is densiy [6]. Here he heory is briefly reminded. Given he se of poins {x (i) } i=..m in he d-dimensional space R d, he non-parameric densiy esimaion a each poin x is given by: bf H,k (x) = n(2π) d/2 H /2 M k( H /2 (x x (i) ) 2 ) (7) i= where k is a kernel profile and H he bandwidh marix. Inroducing he noaion leads o he densiy gradien : g(x) = k (x) b f H,k (x) = H b fh,g(x) m H,g(x) (8)

4 where m H,g is he mean shif vecor, i= x(i) g` H /2 (x x (i) ) 2 m H,g(x) = i= g` H x. (9) /2 (x x (i) ) 2 Using exacly his displacemen vecor a each sep guaranies convergence o he local maximum of he densiy. Wih a d-variae Gaussian kernel, equaion 9 becomes i= m H,g(x) = x(i) exp( 2 D2 (x, x (i) ; H)) i= exp( x (0) 2 D2 (x, x (i) ; H)) where D 2 (x, x (i) ; H) (x x (i) ) T H (x x (i) ) () is he Mahalanobis disance from x o x (i). Assume now ha he d-dimensional space can be decomposed as he Caresian produc of S (3 in our case) independen spaces associaed o differen ypes of informaion (e.g. posiion, color...), also called feaure spaces or domains, wih dimensions d s, s =... S (where P S s= ds = d). Because he differen ypes of informaion are independen, he bandwidh marix H becomes H = diag[h... H S] and hus he mean shif vecor can be rewrien as P Q M i= m H,g(x) = x(i) S s= exp( 2 D2 (x s, x (i) s ; H s)) Q S i= s= exp( x 2 D2 (x s, x (i) s ; H s)) (2) where x (i)t = (x (i) T (i) T,..., x S ) and x T = (x T,..., x T S ). The mean shif filering is obained by successive compuaions of equaion 0 or 2 and ranslaion of he kernel according o he mean shif vecor. This procedure converges o he local mode of he densiy [6] Bandwidh selecion The pariion of he feaure space is obained by grouping ogeher all he daa poins whose associaed mean shif procedures converged o he same mode. The qualiy of he resuls highly depends on he choice of he bandwidh marix H. In [5], Comaniciu proposes o find he bes bandwidhs wihin a range of B predefined marices {H (b), b =... B}. Mean Shif pariioning is firs run a each scale (for b varying from o B). For each daa poin x (i), an analysis of he sequence of clusers o which he poin is associaed is performed. The scale for which he cluser is he mos sable is seleced, along wih associaed bandwidh, for daa poin x (i). Therefore, he algorihm can be decomposed in wo seps. The firs one is called bandwidh evaluaion a he pariion level. I consiss in finding a parameric represenaion of each cluser in order o do he comparisons. The second sep called evaluaion a he daa level is he analysis of cluser sequences a each daa poin. An ieraive algorihm dedicaed o bandwidh selecion for mixed feaure spaces has been derived from his mehod [4]. Bes bandwidhs are hen ieraively found for posiion, color and moion. The range of predefined marices for color and moion is direcly compued from image noises. Inroducing C he se of pairs of neighboring poins of he grid, C is cardinal, I ds he ideniy marix of dimension d s, and he mean and sandard deviaion : β s = α s = C s C (i,j) C (i,j) C ( x (i) s x (i) s x (j) s, (3) x (j) s α s) 2, (4) he range of marices for color (s = 3) and moion (s = 2) of size d s can be wrien as H (b) s = (α + 2bβ B )I d s, b =... B. (5) The range of marices for posiion reads: H (b) = 4b B ( w N, h N )I d, b =... B. (6) The bes bandwidh obained a he end of he bandwidh selecion algorihm will be denoed as H = diag[ H, H 2, H 3 in he sequel. A he end of he mean shif clusering procedure [4] several clusers are obained, each corresponding o a moving objec or objec par. We reain only large enough clusers (e.g., wih more han 5 grid poins). 5. Segmenaion Segmening he objec associaed o a given cluser amouns o assigning a label l p, eiher background or objec, o each pixel p of he image. This problem can be reformulaed ino he graph cu framework as a bi-pariioning problem. Recenly graph cus have been increasingly used in image segmenaion. The reason for such a populariy is ha he exac maximum a poseriori (MAP) of a wo label pairwise Markov Random Field (MRF) can be compued in polynomial ime using min-cu/max-flow algorihms [9]. In seminal paper [3], Boykov e al. inroduce an ieraive foreground/background segmenaion sysem based on his principle, using hard consrains provided by he user. Here we can direcly learn some properies of he objec from he grid poins belonging o is cluser. These poins are called inliers. The energy funcion o minimize is defined as: E (L) = γ c ln(pr(i (c) (p) l p)) γ m ln(pr(f (p) l p))+ λ p P (p,q) V exp I (g) (p) I (g) (q) 2 σ 2. p G ( δ(lp, lq)) dis(p, q) (7) where L is he se of all he labels l p, p P, V is he se of unordered pairs (p, q) of neighboring elemens of P and I (c) is he original RGB color image convered o YUV color space. The parameers γ m, γ c, λ are some weigh consans discussed below.

5 The wo firs erms of he cos funcion are based on pixel-wise modeling of color and moion feaures disribuions. Moion erm only concerns he poins of he grid. For boh color and moion, objec disribuions are buil from hisograms on he inliers. For he background, hisograms are buil as follows. For color i is compued on he all image whereas for moion i is only compued on he grid. In [2], auhors have shown ha i is possible o force some pixels o belong o he objec or o he background. Here we force inliers o belong o he objec. Because for moion we only ake poins of he grid, we chose o se he parameers γ c and γ m such ha γ c = and γ m = w + h 2N. (8) The parameer σ in he hird energy erm can be relaed o noise [8]. Here we already have is approximae value from he bandwidh selecion in mean shif clusering. Thus we chose σ as σ 2 = H 3 2. (9) The value of parameer λ has no been really sudied in lieraure. To avoid a possible sauraion of all binary edges in he max-flow procedure, we fix here is value as: λ = argmin p γ c p P ln(pr(i (c) (p) l p))+γ m ln(pr(f (p) l p)). p G (20) A he end, we obain one segmenaion for each cluser. 6. Resuls of objecs deecion Exising mehods for moion deecion are limied o small or regular moion in he background, o small moion of he objecs, or o rigid layers. To demonsrae he srengh of our mehod we show resuls on hree challenging sequences for which hese consrains do no necessarily hold. In figures -3, he firs column shows several frames of he video sequences. The second and hird columns display, overlaid on each of hese frames, he resuls of he mean shif clusering algorihm and of he segmenaion algorihm respecively. Differen colors are used o represen he differen moving objecs of he scene. Noe ha here is no emporal consisency eiher beween objecs or beween heir colors. The assigned colors only depend on he order in which our algorihm deecs he objecs. The firs video (figure ) is a ennis sequence which includes a complex background moion wihin he specaors, he rapid moion of he player and his racke, and he fas pan and zoom-ou of he camera. Despie his complex dynamic conen, our algorihm deeced he player in each frame of he sequence. On he firs frame presened here, he racke and he body have a compleely differen moion and herefore hey are deeced separaely. The second resuls (figure 2) are on a sequence of a waer skier. The dynamic conen of waer regions is all he more complex since hey include projecions behind he skier. Good resuls on his video are parly due o he use of p- value for he validaion of opical flows. Noe however ha par of he waer is someimes deeced as a moving objec. The las sequence presened here (figure 3) shows a person driving a car. This ype of sequences is very difficul as various complex moions appear hrough he window, wih sudden speed, illuminaion and parallax changes. Our algorihm was noneheless able o capure ineresing foreground objecs, i.e., he face and he hands, when hey were moving. In he second frame, he face sopped moving and herefore is no deeced. As wih porions of waer in he previous example, objecs behind he window are someimes deeced by he mean shif clusering algorihm. We believe ha adding emporal consisency or racking would allow he rejecion of such ransien deecions while locking on ineresing objecs even if hey sop moving. Noe also ha some inliers from he grid remain isolaed afer graphcu segmenaion (such poins, hardly visible in he final ransparen overlay, can be seen on close-ups). They could be easily eliminaed in a pos-processing sep (e.g., reaining only larges conneced componens), as ofen done in saic image segmenaion. 7. Conclusion and fuure work We have presened a echnique o deec and segmen moving objecs in complex dynamic scenes sho by possibly moving cameras. As we only work on a sub grid of pixels, and because we do no model he background, his mehod is no compuaionally and memory expensive. The use of spaial, dynamic and phoomeric feaures allows he exracion of moving foreground objecs even in presence of illuminaion changes and fas variaions in he background. Disincive ingrediens of our approach include he use of p-value o validae opical flow vecors, he use of auomaic mulidimensional bandwidh selecion in he mean shif clusering algorihm and he use of sparse moion daa in a MAP-MRF framework. I is worh emphasizing ha he parameers involved in he preliminary moion compuaions (opic flow and parameric dominan moion) are fixed o he same values in all experimens, while he oher parameers (for clusering and segmenaion) are auomaically seleced. We plan in he fuure o add emporal consisency eiher on a frame-o-frame basis or wihin a racker whose (re)iniializaion would rely on deecion maps. References [] M. Black and P. Anandan. A framework for he robus esimaion of opical flow. Proc. In. Conf. Compuer Vision,

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