Keeping Multiple Objects in the Field of View of a Single PTZ Camera

Transcription

1 29 Amercan Control Conference Hyatt Regency Rverfront, St Lous, MO, USA June -2, 29 FrC3 Keepng Multple Objects n the Feld of Vew of a Sngle PTZ Camera N R Gans, G Hu and W E Dxon Abstract Ths paper ntroduces a novel vsual servo controller desgned to keep multple movng objects n the camera feld-of-vew usng a pan/tlt/zoom camera In contrast to most vsual servo controllers, there s no goal pose or goal mage In ths paper, a set of task functons are developed to regulate the mean and varance of a set of mage features Regulatng these task functons wll deter feature ponts from leavng the camera feld-of-vew An addtonal task functon s used to mantan a hgh level of moton perceptblty, whch ensures that desred feature pont veloctes can be acheved To provde proper control of a pan/tlt/zoom camera, an mage Jacoban s developed utlzng actuaton of the focal length Smulatons of several object trackng tasks have verfed the performance of the proposed method I INTRODUCTION The use of mage nformaton n feedback control, commonly referred to as vsual servo (VS) control, s an establshed and dverse feld There are many approaches, ncludng mage based vsual servong (IBVS), poston based vsual servong, parttoned methods and swtchng methods See [], [2] and the references theren for tutorals and hstorcal dscusson of these technques Most vsual servo control methods have a common objectve, to regulate a camera, robot manpulator, or unmanned vehcle to a desred pose or along a desred trajectory However, many vsonbased control tasks do not requre a specfc goal pose or trajectory One task that s not well characterzed by a goal pose or mage s keepng multple, movng objects n the feld-ofvew (FOV) Consder the scenaro of crowd survellance A camera vews the crowd, and performs target segmentaton and trackng methods to localze ndvduals of nterest vsble n the mage As the crowd moves and dsperses, the method presented n ths paper wll move the camera n an attempt to keep all ndvduals n the FOV A smlar scenaro nvolves trackng several unmanned vehcles and landmarks The method s rooted n classc IBVS [] [4], however there s no goal mage or goal feature trajectory Instead of task functons based on current and goal mages, the proposed method regulates a seres of low dmensonal task Ths research s supported n part by the NSF CAREER award CMS and by the US Department of Energy grant number DE-FG4-86NE37967 Ths work was accomplshed as part of the DOE Unversty Research Program n Robotcs (URPR) Ths research was performed, n part, whle Ncholas Gans held a Natonal Research Councl Research Assocateshp Award at the Ar Force Research Laboratory Ncholas Gans s wth the Natonal Research Councl and Ar Force Research Laboratory, Emal: ngans@ufledu Guoqang Hu s wth the Department of Mechancal and Nuclear Engneerng, Kansas State Unversty, Emal: gqhu@ksuedu Warren Dxon Department of Mechancal and Aerospace Engneerng, Unversty of Florda, Emal: wdxon@ufledu functons based on current mage features The desred task functon velocty s mapped to a tme-varyng feature velocty through correspondng task functon Jacobans These task functon Jacobans are underdetermned and are sutable for task-prorty knematc control [3], [5], [6] The tme-varyng mage feature velocty s mapped to camera motons through IBVS methods The resultng controller allows feature ponts to move wthn the mage, and the camera wll move to deter feature ponts from leavng the FOV Our ntal developments n ths feld [7] ntroduced task functons based on mean, varance and perceptblty of feature pont coordnates n the mage Please refer to [7] for a lterature revew, dscusson and comparson wth other vsual servong methods that use moments of feature ponts, as well as a dscusson the use of task-functon knematc contro n moble robot and vsual servo control The work n [7] focused on the general case of a sx degree-of-freedom (DOF), arborne moble camera The most promnent camera n survellance tasks s the pan/tlt/zoom (PTZ) camera, whch can rotate about two axes and alter ts focal length to alter the perspectve zoom of the mage In ths current development, we extend our earler efforts to nclude the unque knematcs of a PTZ camera, ncludng the development of a 3DOF mage Jacoban for feature ponts that ncludes the actuaton of the focal length as a varable A zoom term was appended to a standard mage Jacoban n [8], though a closed-form expresson for the zoom term s not gven A 3DOF mage Jacoban correspondng to a PTZ camera watchng a sphercal target was developed n [9] Sectons II and III ntroduce background nformaton related to the camera model and control approach Sectons IV- A through IV-C ntroduce the task functons used to generate the desred feature velocty Smulatons of tasks are gven n Secton V to demonstrate the performance of ths controller II CAMERA MODEL AND IMAGE JACOBIAN FOR PAN/TILT/ZOOM CAMERAS An mage s a functon of the relatve pose between the scene and camera, and the camera ntrnsc parameters Consder a camera wth coordnate frame F c (t) The frame s orented such that the camera optcal axs corresponds to the z-axs of F c (t), and the x-axs and y-axs are the horzontal and vertcal drectons of the mage surface The camera captures mages of a collecton of k vsble feature ponts These ponts have Eucldean coordnates M (t) R 3 defned as M =[X,Y,Z ] T, { k} /9/$25 29 AACC 5259

2 n the camera frame The pont M (t) projects to a pont n the feature pont plane wth coordnates λ M = = x y m = () Z λ λ λx Z λy Z λ where m (t) R 2 s the mage coordnate of the th feature pont and λ(t) s the focal length of the camera Gven the collecton of k feature ponts n the mage, a feature state vector m(t) R 2k and state velocty vector ṁ(t) R 2k are defned as m = [x,y,,x k,y k ] T ṁ = [ẋ, ẏ,, ẋ k, ẏ k ] T The extracton of such ponts could come from a varety of algorthms, such as trackng dstngushable ponts on movng targets [], [], the centrod of segmented blobs [2] or the corner ponts of boundng rectangle of tracked movng targets [3] A PTZ camera cannot translate, but can rotate about two axes and alter the focal length of ts lens Ths work assumes that the center of rotaton corresponds to the orgn of the camera frame Most vsual servong approaches assume a constant focal length, and many scale the system such that λ = In the subsequent development here, a tme-varyng λ(t) s ntroduced as an actuaton to alter the mage Increasng λ has the effect of zoomng nto the mage, e objects n the mage wll appear larger and feature ponts move toward the edge of the FOV Decreasng λ wll zoom out of the mage, e objects wll appear smaller and feature ponts wll move toward the mage center Whle ths s smlar to the effect of translatng along the camera optcal axs, there are mportant dfferences Most notably, translaton s not commutatve wth rotaton, but zoom s commutatve Incorporatng λ nto a VS control system requres developng a novel mage Jacoban If the vewed objects are not movng n the world frame, the dervatve of M (t) as a functon of camera velocty s gven by Ṁ = ω M (2) where ω (t) =[ω x, ω y, ] T R 3 s the angular velocty of the camera Equaton (2) can be rewrtten as Ẋ Z ω y Ẏ Ż = Z ω x X ω y Y ω x (3) The dervatve of () s gven by µ d λ M = λ ³Ṁ dt Z Z 2 Z M Ż + λ M Z (4) Combnng the terms n (3) wth the top two rows (4) gves ẋ = x y ω x (λ 2 + x 2 )ω y + λx ẏ λ (λ 2 + y 2)ω x x y ω y + λy = x y (λ 2 + x 2 ) x ω x λ (λ 2 + y 2) x ω y y y λ = L λ v ptz (5) where L λ (t) s the mage Jacoban for pont for a PTZ h T camera, and v ptz (t) R 3 = ω x, ω y, λ Itcanbeseen that L λ (t) matches the frst three rows of the Jacoban for a sphercle target n [9] The tme dervatve of a feature pont vector m(t) s gven by ṁ = L λ v ptz where L λ (t) R 2k 3 = L T λ,,lt kλ T In the case that the feature ponts are not statc n an nertal world frame (eg, the feature ponts are tracked on movng objects), the tme dervatve of the feature ponts s gven by ṁ = L λ v ptz + ε (6) where ε(t) s an unknown functon caused by the targets moton We make a practcal assumpton that ε(t) s bounded The frst two columns of L λ (t) are the same as the fourth and ffth columns of the classc sx -DOF mage Jacoban [], [3] One notable feature of L λ (t) s that t s not dependent on feature depths Z (t) The sx-dof mage Jacoban ncludes the depths Z (t) as varables The feature pont depths must be known or accurately estmated for sx-dof IBVS, and IBVS s brttle to depth estmaton errors [4] As seen n (5), the depths do not need to be known, whch ncreases robustness and decreases computatonal complexty III TASK FUNCTION-BASED KINEMATIC CONTROL The control objectve s to keep a set of feature ponts wthn the camera FOV wthout a specfc camera pose Motvated by ths objectve, a set of task functons are defned usng mage feature coordnates By regulatng the task functons, feature ponts can be nhbted from leavng the FOV However, the task functons may compete n the sense that reducng the error of one task functon may ncrease the error of another To avod competton between task functons, task-prorty knematc control [5], [6] s used Let φ(t) R l denote a task functon of the feature pont coordnates m (t) as φ = f(m) wth dervatve kx f φ = ṁ = J(m)ṁ (7) m where J(m) R l 2k s the task Jacoban matrx The task functons developed subsequently are of dmenson l 2 The task s to drve the feature ponts along a desred velocty ṁ φ (t) such that φ(t) follows a desred trajectory φ d (t) Gven the underdetermned structure of the Jacoban 526

3 matrx, there are nfnte solutons to ths problem The typcal soluton based on the mnmum-norm approach [6] s gven as h ṁ φ = J φd γ(φ φ d ) = J T JJ T h φd γ(φ φ d ) (8) where γ s a postve scalar gan constant, and J (m) R 2k l denotes the mnmum-norm general nverse of J (m) Based on (8), the camera velocty v ptz (t) s desgned as v ptz = L + λ ṁφ = L T λ L λ L T λ ṁ φ (9) where L + λ (t) R3 2k denotes the least squares general nverse of L λ (t) Note that J (m) s underdetermned (e more columns than rows), and L λ (t) s overdetermned (e more rows than columns) Therefor, the general nverses have dfferent solutons and are denoted dfferently A set of task functons can be combned n varous ways The smplest approach s to add the desred feature velocty from each task functon to create the total desred feature pont velocty For example, consder two tasks φ a (t) and φ b (t) wth resultng desred feature veloctes ṁ a (t) and ṁ b (t) The camera velocty s then gven as v ptz = L + λ (ṁ a + ṁ b ) Snce the task functon veloctes are undetermned, an optonal method s to choose one task as prmary, and project the other tasks nto the nullspace of the prmary task dervatve [5], [6] as v ptz = L + λ ṁa +(I J aj a )ṁ b = L + λ ³J a φ a +(I J aj a )J b b φ () where J a (m a ) and J b (m b ) are the task Jacoban matrces wth respect to φ a (t) and φ b (t), respectvely The approach n () wll prevent the velocty vectors from competng and negatng each other, as the prmary task wll always be accomplshed Lower prorty control tasks wll be acheved f they do not oppose hgher prorty tasks Tertary, quaternary, etc tasks can be prortzed by repeatng ths process and projectng each subsequent task nto the nullspace of the precedng task Jacobans IV CONTROL DEVELOPMENT Three task functons are presented as part of a task-prorty knematc controller Two task functons are desgned to regulate the mean and varance of the feature pont coordnates Regulatng the mean at the camera center wll keep the feature ponts centered n the FOV Regulatng the varance wll restrct the dstance between the feature ponts and keep feature ponts away from the edge of the FOV A thrd task functon maxmzes moton perceptblty, whch ensures desred mage veloctes can be met These task functons are cascaded through nullspace projecton and mapped to camera velocty, as descrbed Secton III Cascadng controllers for mean and perceptblty wth a controller for varance wll deter objects from leavng the FOV Abeneft of the mean and varance task functons s that no feature pont trackng, matchng or regstraton needs to occur, and the order of feature ponts n the feature vector m(t) s not mportant Ths beneft s due to the fact that all specfc pont nformaton s lost by takng the mean and varance Some feature extracton methods, such as corner detecton or takng the centrod of blob segments, do not match or order features between mages wthout addtonal algorthms Matchng can be a dffcult problem, so ths s a notable advantage If varance s regulated to a sutably small goal value, a subset of feature ponts can be guaranteed to stay n vew Gven a dstrbuton wth mean x and varance σ 2 x, Chebyshev s nequalty states [5] that no more than /k 2 of the values are more than k standard devatons away from the mean Specfcally, Chebyshev s nequalty states that at least 75% of all values are wthn two standard devatons of the mean, and at least 89% of values are wthn three standard devatons For a normally dstrbuted random process, these lmts are tghter such that approxmately 95% of all values wll be wthn two standard devatons, and 997% of all values wll be wthn three standard devatons Consder a camera wth a 52x52 pxel FOV regulatng the varance to 86 2 wll ensure that at least 89% of all ponts are n the FOV Features may be lost due to leavng the FOV, occlusons, or falure of the mage processng routne that extracts features If ths occurs, the last known coordnates can be used to attempt to recapture t at a later tme, or the pont can be dscounted from the mean and regulaton contnues wth the remanng ponts A Task Functon for the Mean of the Image Ponts Controllng the mean of the feature pont coordnates helps to ensure the feature ponts are centered around a poston n the mage plane Let φ m (t) R 2 denote a task functon defned as the sample mean φ m = kx m = m k Thetmedervatveofφ m (t) s gven by φ m = kx φ m ṁ = J m ṁ k m = J m (L λ v ptz + ε) where J m (t) R 2 2k s a task functon Jacoban defned as J m = k [I 2,,I 2 ] () where I 2 s the 2 2 dentty matrx and s repeated k tmes In the general case, the objectve s to regulate the mean to track a desred trajectory, helpng to ensure the feature ponts are centered around a specfc, tme-varyng pont n the mage Defne a desred task functon trajectory φ md (t), wth a known, smooth dervatve φ md (t) PID control can be used to generate a feature velocty that wll track the 526

4 desred value of φ md (t) Ths feature velocty s denoted ṁ m (t) R 2k, and s gven by Z t ṁ m = J m µγ mp φ me + γ m φ me dt d +γ md dt φ me φ md (2) where φ me (t) =φ m (t) φ md (t) and γ mp, γ m,γ md R are constant gans If the desred trajectory φ md (t) s smplfed to a constant set value φ md, equaton (2) can be smplfed as Z t ṁ m = J m µγ d mp φ me + γ m φ me dt + γ md dt φ me B Task Functon for the Varance of the Image Ponts Regulatng the varance of the feature pont coordnates wll regulate the spread of the feature ponts n the mage That s, regulatng the varance wll control how far the feature ponts drft from the mean value A task functon φ v (t) R 2 s gven by the sample varance φ v = kx (x x) 2 k (y ȳ) 2 where x (t) and ȳ (t) are the mean of all the x and y components of m (t), { k}, respectvely To fnd the varance task functon Jacoban, consder the partal dervatve of φ v (t) wth respect to x (t) φ v x = " # kx (x x) 2 x k " # = 2 (x x) kx (x x) k k = 2 k [x x] Repeatng the above smplfcatons for all x (t),y (t), { k}, gves the tme dervatve of φ v (t) as φ v = J v ṁ = J v (L λ v ptz + ε) where L λ (t), v ptz (t) and ε(t) weregvenn(6),andj v (t) R 2 2k sataskfunctonjacobangvenby J v = 2 k x x y ȳ, x 2 x y 2 ȳ,, x k x y k ȳ To regulate the varance to a desred trajectory φ vd (t) (wth a known, smooth dervatve φ vd (t)) the feature pont velocty ṁ v (t) R 2k can be desgned by followng the method n SectonIV-Atogve Z t ṁ v = J v µγ d vp φ ve + γ v φ ve dt + γ vd dt φ ve φ vd (3) where φ ve (t) =φ v (t) φ vd (t) and γ vp, γ v,γ vd R are constant gans An mportant consequence of usng mean and varance as task functons s that the control laws for mean and varance do not nterfere wth each other Ths means that the tasks of regulatng mean and varance wll not nterfere wth each other and ṁ m + ṁ v canbeusednplaceofthenullspace projecton of equaton () Theorem : Gven the Jacoban matrces J m and J v, J m J v = J v J m = Proof: The pseudo nverse J v(t) R 2k 2 can be wrtten n closed form and multpled wth () to gve x x (x x) 2 J m J v = 2 = 2 T y x (y ȳ) 2 x k x (x x) 2 y k x k x k x (x x) 2 kȳ kȳ (y ȳ) 2 (y ȳ) 2 = It can smlarly be shown that J v J m = Combnng (), (2) and (3) and the result of Theorem, the resultng feature pont velocty from prortzng the taskssgvenby ṁ = ṁ m +(I J mj m )ṁ v = J m φ m +(I J mj m )J v φ v = J m φ m + J vφ v = ṁ m + ṁ v C Task Functon for Perceptblty of Image Ponts Sharma and Hutchnson presented the concept of moton perceptblty n [6] Perceptblty gves a measure of how well a camera can perceve the moton of objects n the FOV Roughly speakng, f perceptblty s hgh, small object or camera veloctes wll result n notable feature veloctes n the mage plane (eg, hgh optcal flow) Ths s especally mportant f there are more than three feature ponts, as the avalable feature pont veloctes are constraned due to an overdetermned mage Jacoban Mantanng a hgh perceptblty helps to ensure a larger span of avalable feature pont velocty vectors Perceptblty s a scalar functon of the mage Jacoban L λ (t), defned as q w v = det(l T λ L Q λ)= 3 σ where σ (t) R + are the sngular values of L λ (t) Maxmzng w v (t) s accomplshed by maxmzng each σ (t) The matrx L T λ (t)l λ (t) R 3 3 s postve defnte and symmetrc, so the egenvalues of L T λ (t)l λ (t) are equal 5262

5 to σ 2 (t) The trace of a matrx s equal to the sum of ts egenvalues Therefore, the trace of L T λ (t)l λ (t) s related to the sngular values by Tr(L T P λ L λ )= 3 σ 2 Increasng the trace of L T λ (t)l λ (t) wll also ncrease the perceptblty The trace of L T λ (t)l λ (t) s gven by Tr(L T P λ L λ ) = k ³ 2x 2 y 2 + y 2 + λ 2 + x 2 + λ 2 + x 2 + y 2 A task functon for perceptblty can be defned as a functon of feature-pont coordnates as φ p = (x 2 + y2 ) Snce t s desred to ncrease Tr(L T λ L λ), regulatng φ p (t) to wll result n ncreasng the trace The tme dervatve of φ p (t) s gven by µ φ p = 2φ 2 P k ẋ p x y ẏ = J p (m)ṁ = J p (m)(l λ v ptz + ε) where L λ (t), v ptz (t) and ε(t) were gven n (6), and J p (m) R 2k s the task functon Jacoban for perceptblty The matrx J p (m) s undefned only for the nongeneral case that, m = To regulate φ p (t) to, the feature pont velocty ṁ p (t) R 2k s desgned as ṁ p = γ p J pφ p (4) where γ p s a postve scalar gan constant As n the mean and varance control laws, a trackng error wll exst for the perceptblty task However, the use of ntegral feedback s not recommended for the perceptblty regulaton due to the fact that φ p (t) s unlkely to ever become zero, leadng to possble ntegrator wndup and related stablty problems D Cascaded Camera Control Law The control objectve s to desgn a knematc controller for a PTZ camera that mantans a set of feature ponts wthn the camera FOV The mean of feature pont coordnates s the best varable to measure the center of the feature ponts As shown n Theorem, regulatng the mean and varance wll not conflct, so they are chosen as the prmary tasks n order to keep the feature ponts centered n the FOV and to restrct the dstance between the feature ponts and the mage center These two tasks wll deter feature ponts from leavng the FOV Hgh perceptblty wll allow these tasks to work more effcently by ensurng larger avalable feature veloctes are avalable For ths reason, ncreasng perceptblty s chosen as the lower prorty task, and t cannot nterfere wth the regulaton of mean or varance The meters 5 5 meters 5 Fg 5 meters 5 3D object moton and camera desgned feature veloctes gven n (2), (3), and (4) are used n the nullspace projecton camera velocty () to gve the overall controller as v ptz = L + λ ṁm + ṁ v + I J mj m I J v J v ṁp = L + λ ṁm + ṁ v + I J mj m J vj v ṁp (5) where the ndependence of ṁ m and ṁ v proved n Theorem has been exploted V SIMULATION RESULTS Smulatons are performed usng the PID controller gven n (2)-(5) Smulatons have been run n a realstc vrtual envronment as well, requrng feature extracton and real tme mage and control processng Moves of these smulatons are avalable at [7] In ths smulaton, a camera observes two rgd, square objects wth dmensons of m m The centers of the two objects are ntally located at coordnates [X, Y, Z] T =[,, ] and [X, Y, Z] T =[,, ] n the world frame The objects move ndependently and the corner of the two squares gve eght feature ponts to track Ths smulaton mmcs the case of a camera trackng corners on two vehcles The camera s fxed at coordnates [, 45, 2] T n the world frame The mean was regulated to the mage center The varance was regulated to [ 2, 2 ] T, e a standard devaton of pxels The gans were selected as γ mp =5, γ m =7 3, γ md = 7, γ vp = 75 5, γ v = 35 7, γ vd = 35 6, γ p = 5 5 The smulaton was executed for 6 seconds at 3 frames per second The square objects moved wth snusodal veloctes along all sx degrees of freedom Fg shows a thrd person vew of the two objects and camera The 3D paths of the corners ponts are shown as dotted lnes Fg 2 depcts the trajectory of the two objects and feature ponts n the mage plane The feature ponts all reman n the FOV The dashed ellpse and square represent the fnal values of the varance and mean, whle the sold ellpse and star represent the goal varance and mean Fg 3 shows the task functon error over tme The mean error s well regulated about zero Whle varance s perodc, t remans bounded about zero error Fg 4 shows the elements of the camera velocty 5263

6 pxels Fg 2 m md (pxels) v vd (pxels) p (untless) Fg pxels 2 Object trajectores n the mage when regulatng varance 2 x y tme (sec) Task functon errors whle trackng two movng targets VI CONCLUSIONS AND FUTURE WORK Ths paper ntroduces a method to track multple movng objects and keep them n the camera FOV usng a Pan/Tlt/Zoom camera A set of underdetermned task functons of sample mean and varance of feature pont coordnates are used to deter feature ponts from leavng the FOV A thrd task functon seeks to maxmze moton perceptblty There s no specfc goal mage or goal pose The underdetermned nature of the task functons allows the camera to move as necessary to regulate the task functons and nhbt feature ponts from approachng the edge of the FOV Ths objectve s n contrast to other vsual servo controllers that requre a specfc goal mage or goal pose Furthermore, to the authors knowledge, ths s the frst use of perceptblty n the feedback loop of a controller Smulatons of several object trackng tasks were performed to demonstrate ths method To accommodate the use of a PTZ camera, a novel mage Jacoban for feature ponts was developed that ncludes focal length as an actuaton One notable beneft of the PTZ camera s that t does not depend on the depth of feature ponts, lke tradtonal mage Jacobans There are several avenues of future work Smulatons are very promsng, but a proper stablty analyss wll be performed The moton of the targets can be estmated usng ang vel (rad/sec) ang vel (rad/sec) d/dt (untless) Fg Camera velocty whle trackng two movng targets a varety of estmaton or adaptve technques, mprovng the performance of the system Expermental analyss s also underway, whch wll nclude target recognton, localzaton and nterframe trackng REFERENCES [] S Hutchnson, G Hager, and P Corke, A tutoral on vsual servo control, IEEE Trans Robot Automat, vol 2, pp 65 67, Oct 996 [2] F Chaumette and S Hutchnson, VsualservocontrolpartI: Basc approaches, IEEE Robotcs and Automaton Mag, vol 3, no 4, pp 82 9, 26 [3] B Espau, F Chaumette, and P Rves, A new approach to vsual servong n robotcs, IEEE Trans Robot Automat, vol 8, pp , June 992 [4] LEWess,ACSanderson,andCPNeuman, Dynamcsensorbased control robots wth vsual feedback, IEEE Trans Robot Automat, vol 3, pp 44 47, Oct 987 [5] Y Nakamura, H Hanafusa, and T Yoshkawa, Task-prorty based redundancy control of robot manpulators, Int J Robotcs Research, vol 9, pp 3 5, 987 [6] S Chavern, Sngularty-robust task-prorty redundancy resoluton for real-tme knematc control of robot manpulators, IEEE Trans Robot Automat, vol 3, no 3, pp 398 4, 997 [7] N R Gans, G Hu, and W E Dxon, Keepng objects n the feld of vew: An underdetermned task functon approach to vsual servong, n Proc IEEE Mult-Conf Systems and Control, pp , 28 [8] K Hosoda, H Moryama, and M Asada, Vsual servong utlzng zoom mechansm, n Proc IEEE Int Conf Robotcs and Automaton, pp 78 83, 995 [9] K Hatano and K Hashmoto, Image-based vsual servo usng zoom mechansm, n Proc SICE Annual Conference, vol 3, pp Vol3, Aug 23 [] J Sh and C Tomas, Good features to track, n Proc IEEE Conf Computer Vson and Pattern Recognton, pp 593 6, 994 [] D Lowe, Object recognton from local scale-nvarant features, n Proc IEEE Int Conf Computer Vson, vol 2, pp 5 57, 999 [2] M Nethammer, A Tannenbaum, and S Angenent, Dynamc actve contours for vsual trackng, IEEE Trans Automat Contr, vol 5, pp , Aprl 26 [3] C Gentle, O Camps, and M Sznaer, Segmentaton for trackng nthepresenceofsevereoccluson, IEEE Trans Image Processng, vol 2, pp 66 78, Feb 24 [4] E Mals and P Rves, Robustness of mage-based vsual servong wth respect to depth dstrbuton errors, n Proc IEEE Int Conf Robotcs and Automaton, vol, pp 56 6, 23 [5] H Stark and J Woods, Probablty and Random Processes wth Applcatons to Sgnal Processng Prentce Hall, 22 [6] R Sharma and S Hutchnson, Moton perceptblty and ts applcaton to actve vson-basedservo control, IEEE Trans Robot Automat, vol 3, no 4, pp 67 67, 997 [7] Vdeos/PTZ VS/ 5264