Realtime Moving Object Detection from a Freely Moving Monocular Camera

Transcription

1 Realtme Movng Object Detecton from a Freely Movng Monocular Camera Abhjt Kundu, C. V. Jawahar and K Madhava Krshna Abstract Detecton of movng objects s a key component n moble robotc percepton and understandng of the envronment. In ths paper, we descrbe a realtme ndependent moton detecton algorthm for ths purpose. The method s robust and s capable of detectng dffcult degenerate motons, where the movng objects s followed by a movng camera n the same drecton. Ths robustness s attrbuted to the use of effcent geometrc constrants and a probablty framework whch propagates the uncertanty n the system. The proposed ndependent moton detecton framework ntegrates seamlessly wth exstng vsual SLAM solutons. The system conssts of multple modules whch are tghtly coupled so that one module benefts from another. The ntegrated system can smultaneously detect multple movng objects n realtme from a freely movng monocular camera. I. INTRODUCTION SLAM nvolves smultaneously estmatng locatons of newly perceved landmarks and the locaton of the robot tself whle ncrementally buldng a map of an unknown envronment. Over the last decade, SLAM has been one of the most actve research felds n robotcs and excellent results have been reported by many researchers [1]; predomnantly usng laser range-fnder sensors to buld 2-D maps of planar envronments. Though accurate, laser range-fnders are expensve and bulky, so lot of researchers turned to cameras whch provde low-cost, full 3-D and much rcher ntutve humanlke nformaton about the envronment. So last decade also saw a sgnfcant development n vson based SLAM systems [2], [3], [4]. But almost all these SLAM approaches assume a statc envronment, contanng only rgd, non-movng objects. Movng objects are taken as nose sources and fltered out. Though, ths may be a feasble soluton n less dynamc envronments, but t becomes unavodable as the envronment becomes more and more dynamc. Also accountng for both the statc and movng objects provdes rcher nformaton about the envronment. A robust soluton to the SLAM problem n dynamc envronments wll expand the potental for robotc applcatons, especally n applcatons whch are n close proxmty to human bengs and other robots. As put by [5], robots wll be able to work not only for people but also wth people. The soluton to the movng object detecton and segmentaton problem wll act as a brdge between the statc SLAM and ts counterpart for dynamc envronments. But, moton detecton from a freely movng monocular camera s an llposed problem and a dffcult task. The movng camera causes every pxel to appear movng. The apparent pxel moton of ponts s a combned effect of the camera moton, ndependent object moton, scene structure and camera perspectve effects. Dfferent vews resultng from the camera moton are connected by a number of multvew geometrc constrants. Ths constrants can be used for the moton detecton task. Those nconsstent wth the constrants can be labeled as movng regons or outlers. We propose a realtme ndependent moton detecton algorthm wth the ad of an onlne vsual SLAM algorthm. The movng object detecton s robust and s capable of segmentng dffcult degenerate motons, where the movng objects s followed by a movng camera n the same drecton. We ntroduce effcent geometrc constrants that helps n detectng these degenerate motons and a probablty framework that recursvely updates feature probablty and takes nto consderaton the uncertanty n camera pose estmaton. The fnal system ntegrates ndependent moton detecton wth vsual SLAM. We ntroduce several feedback paths between these modules, whch enables them to mutually beneft each other. A full perspectve camera model s used, and we do not have any restrctve assumptons on the camera moton or envronment. Unlke many of the exstng works, the proposed method s onlne and ncremental n nature and scales to arbtrarly long sequences. We also descrbe how ths system can be used to constran and speed-up object detecton algorthms, where detecton of specfc object category lke person s requred. Fnally we show expermental results of ths algorthm on real mage datasets. II. RELATED WORKS The task of movng object detecton and segmentaton, s much easer f a stereo sensor s avalable, whch allows addtonal constrants to be used for detectng ndependent moton [6], [7], [8]. However the problem s very much ll-posed for monocular systems. The problem of moton detecton and segmentaton from a movng camera has been a very actve research area n computer vson communty. The multvew geometrc constrants used for moton detecton, can be loosely dvded nto four categores. The frst category of methods used for the task of moton detecton, reles on estmatng a global parametrc moton model of the background. These methods [9], [10], [11] compensate camera moton by 2D homography or affne moton model and pxels consstent wth the estmated model are assumed to be background and outlers to the model are defned as movng regons. However, these models are approxmatons whch only holds for the restrcted cases of camera moton and scene structure.

2 The problems wth 2D homography methods led to planeparallax [12], [13] based constrants. The planar-parallax constrants, represents the scene structure by a resdual dsplacement feld termed parallax wth respect to a 3D reference plane n the scene. The plane-parallax constrant was desgned to detect resdual moton as an after-step of 2D homography methods. Also they are desgned to detect moton regons when dense correspondences between small baselne camera motons are avalable. Also, all the planar-parallax methods are neffectve when the scene cannot be approxmated by a plane. Though the planar-parallax decomposton can be used for egomoton estmaton and structure, the tradtonal mult-vew geometry constrans lke eppolar constrant n 2 vews or trllnear constrants n 3 vews and ther extenson to N vews have proved to be much more effectve n scene understandng as n structure from moton (SfM) and vsual SLAM. Ths constrants are well understood and are now textbook materals [14]. In realtme monocular vsual SLAM systems, movng objects have not yet been dealt properly. We found the followng three works for vsual SLAM n dynamc envronments: a work by Sola [15] and two other recent works of [16] and [17]. Sola [15] does an observablty analyss of detectng and trackng movng objects wth monocular vson. He proposes a BCamSLAM [15] soluton wth stereo cameras to bypass the observablty ssues wth mono-vson. In [16], a 3D object tracker runs parallel wth the monocular camera SLAM [2] for trackng a predefned movng object. Ths prevents the vsual SLAM framework from ncorporatng movng features lyng on the movng object. But the proposed approach does not perform movng object detecton; so movng features apart from those lyng on the tracked movng object can stll corrupt the SLAM estmaton. Also they used a model based tracker, whch can only track a prevously modeled object wth manual ntalzaton. The work by Mglore et al. [17] mantans two separate flters: a monoslam flter [2] wth the statc features and a bearng only tracker for the the movng features. As concluded by Mglore et al., the man dsadvantage of ther system s the nablty to obtan an accurate estmate of the movng objects n the scene. Ths s due to the fact that they mantan separate flters for trackng each ndvdual movng feature, wthout any analyss of the structure of the scene; whch for e.g can be obtaned from clusterng ponts belongng to same movng object or performng same moton. Ths s also the reason that they are not able to use the occluson nformaton of the tracked movng object, for extendng the lfetme of features as n [16]. Prevously n [18], we used robot odometry to estmate the camera moton, whch was then used to detect ndependently movng objects n the scene. In ths work we extend that work to freely movng monocular camera, wthout any ad from odometry or IMU knd of devces. III. SYSTEM OVERVIEW In the frst step, sparse salent features are detected and tracked through the mage sequence. An onlne vsual SLAM algorthm estmates the camera trajectory and 3D structure usng the feature tracks. Between any two vews, relatve camera moton and locatons of features s used to evaluate the geometrc constrants, as detaled n Sec. IV-A. A recursve Bayes flter s used to compute the probablty of the feature beng statonary or dynamc through the geometrc constrants. The present probablty of a feature beng dynamc s fused wth the prevous probabltes n a recursve framework to gve the updated probablty of the features. The probabltes also take care of uncertanty n pose estmaton by the vsual SLAM. Features wth hgh probabltes of beng dynamc are ether msmatched features arsng due to trackng error or features belongng to some ndependently movng objects. Ths resdual feature tracks are then clustered nto ndependently movng enttes, usng spatal proxmty and moton coherence. A. Feature Trackng In order to detect movng objects, we should be able to get feature tracks on the movng bodes also. Ths s challengng as dfferent bodes are movng at dfferent speeds. Thus, contrary to conventonal SLAM, where the features belongng movng objects are not mportant, we need to pay extra cauton to feature trackng n ths scenaro. In each mage, a number of salent features (FAST corners [19]) are detected, at dfferent mage pyramdal levels whle ensurng the features are suffcently spread all over the mage. A patch s generated on these feature locatons and are matched across mages on the bass of zero-mean SSD scores to produce feature tracks. A number of constrants s used to mprove feature matchng. When a match s found, we try to match that feature backward n the orgnal mage. Matches, n whch each pont s the other s strongest match are only taken as vald. 3D reconstructon by vsual SLAM enables the use of addtonal constrants. For the 3D ponts, whose depth s computed from the vsual SLAM module, the 1D eppolar search s reduced to just around the projecton of the 3D pont on the mage wth predcted camera pose. Also wth the knowledge of camera relatve pose and depth of a feature, an affne warp can be performed on the mage patches to mantan vew nvarance from the patch s frst and current observaton. B. Vsual SLAM Framework The method proposed s ndependent of the SLAM algorthm used. However, we chose the bundle adjustment vsual SLAM [20], [4], [21] framework over the flter based approaches [2], [22]. Apart from accuracy benefts [23], the bundle adjustment vsual SLAM methods extracts as much correspondence nformaton as possble compared to very sparse map (about features per frame) n flter based approaches. Our mplementaton closely follows to that of [20], [4]. In bref, a 5-pont algorthm [24] wth RANSAC s used to estmate the ntal eppolar geometry, and subsequent pose s determned wth 3-pont resecton [25]. Some of the frames

3 are selected as key-frames, whch are used to trangulate 3D ponts. The set of 3D ponts and the correspondng keyframes are used n by the bundle adjustment process to teratvely mnmze reprojecton error. The bundle adjustment s ntally performed over the most recent keyframes, before attemptng a global optmzaton. The whole algorthm s mplemented as two-threaded process, where one thread performs tasks lke camera pose estmaton, key-frame decson and addton, another back-end thread performs optmzes ths estmate by bundle adjustment. IV. INDEPENDENT MOTION DETECTION Usng camera relatve moton and feature tracks, the task s to assgn each feature tracks a probablty of beng dynamc or statc. Effcent geometrc constrants are used to form these probablstc ftness scores. Wth each new frame, the probabltes of feature beng dynamc s fused wth the prevous probabltes n a recursve framework to gve the updated probablty of the features. Features wth hgh probablty of beng dynamc are assgned to one of the ndependently movng objects. A. Geometrc Constrants Eppolar constrant s the commonly used constrant that connects two vews. Reprojecton error or ts frst order approxmaton called Sampson error, based on the eppolar constrant s used throughout the structure and moton estmaton by the vsual SLAM module. Bascally they measure how far a feature les from the eppolar lne nduced by the correspondng feature n the other vew. Though these are the gold standard cost functons for 3D reconstructon, t s not good enough for ndependent moton detecton. If a 3D pont moves along the eppolar plane formed by the two vews, ts projecton n the mage move along the eppolar lne. Thus n spte of movng ndependently, t stll satsfes the eppolar constrant. Ths s depcted n Fg. 1. Ths knd of degenerate moton, s qute common n real world scenaros, e.g camera and a object are movng n same drecton as n camera mounted n car movng through a road, or camera-mounted robot followng behnd a movng person. To detect degenerate moton, we make use of the camera moton and 3D structure, to estmate a bound n the poston of the feature along the eppolar lne. We descrbe ths as Flow Vector Bound (FVB) constrant. Fg. 1. LEFT: The world pont P moves non-degenerately to P and hence x, the mage of P does not le on the eppolar lne correspondng to x. RIGHT: The pont P moves degenerately n the eppolar plane to P. Hence, despte movng, ts mage pont les on the eppolar lne correspondng to the mage of P. 1) Flow Vector Bound (FVB) Constrant:: For a general camera moton nvolvng both rotaton and translaton R, t, the effect of rotaton can be compensated by applyng a projectve transformaton to the frst mage. Ths s acheved by multplyng feature ponts n vew 1 wth the nfnte homography H = KRK 1 [14]. The resultng feature flow vector connectng feature poston n vew2 to that of the rotaton compensated feature poston n vew1, should le along the eppolar lnes. Now assume that our camera translates by t and p n,p n+1 be the mage of a statc pont X. Here p n s normalzed as p n = (u, v, 1) T. Attachng the world frame to the camera center of the 1st vew, the camera matrx for the vews are K[I 0] and K[I t]. Also, f z s depth of the scene pont X, then nhomogeneous coordnates of X s zk 1 p n. Now mage of X n the 2nd vew, p n+1 = K[I t]x. Solvng we get, [14] p n+1 = p n + Kt (1) z Equaton 1 descrbes the movement of the feature pont n the mage. Startng at pont p n n I n t moves along the lne defned by p n and eppole, e n+1 = Kt. The extent of movement depends on translaton t and nverse depth z. From equaton 1, f we know depth z of a scene pont, we can predct the poston of ts mage along the eppolar lne. In absence of any depth nformaton, we set a possble bound n depth of a scene pont as vewed from the camera. Let z max and z mn be the upper and lower bound on possble depth of a scene pont. We then fnd mage dsplacements along the eppolar lne, d mn and d max, correspondng to z max and z mn respectvely. If the flow vector of a feature, does not t le between d mn and d max, t s more lkely to be an mage of an ndependent moton. The structure estmaton from vsual SLAM module helps n reducng the possble bound n depth. Instead of settng z max to nfnty, known depth of the background enables n settng a more tght bound, and thus better detecton of degenerate moton. The depth bound s adjusted on the bass of depth dstrbuton along the partcular frustum. The probablty of satsfyng flow vector bound constrant P (F V B). can be computed as 1 P (F V B) = ( ) 2β (2) F V dmean 1 + d range Here d mean = d mn + d max and d range = d max d mn. 2 2 d mn and d max are the bound n mage dsplacements, The dstrbuton functon s smlar to a Butterworth bandpass flter. P (F V B) has a hgh value f the feature les nsde the bound gven by FVB constrant, and the probablty falls rapdly as the feature les outsde the bound. Larger the value of β, more rapdly t falls. In our mplementaton, we used β = 10. B. Computng Independent Moton Probablty In ths secton we descrbe a recursve formulaton based on Bayes flter to derve the probablty of a world pont and

4 hence ts projected mage pont beng classfed as statonary or dynamc. The moton nose and mage pxel nose f any are bundled nto a Gaussan probablty dstrbuton of the eppolar lnes as derved n [14] and denoted by EL = N(µ l, l ) where EL refers to the set of eppolar lnes correspondng to mage pont, and N (µ l, l ) refers to the standard Gaussan probablty dstrbuton over ths set. Let p n be the th pont n mage I n. The probablty that p n s classfed as statonary s denoted as P (p n I n, I n 1 ) = P n,s (p ) or P n,s n short, where the suffx s sgnfyng statc. Then, wth Markov approxmaton and recursve probablty update of a pont beng statonary gven a set of mages can be derved as P (p n I n+1, I n, I n 1 ) = η s P n+1,s P n,s Here η s s normalzaton constant that ensures the probabltes sum to one. The term P n,s can be modeled to ncorporate the dstrbuton of the eppolar lnes EL. Gven an mage pont p n 1 n I n 1 and ts correspondng pont p n n I n then the eppolar lne that passes through p n s determned as l n = e n p n. The probablty dstrbuton of the feature pont beng statonary or movng due to eppolar constrant s defnes as P EP,s = (2π ) 0.5 exp( 0.5(l n µ n ) τ l 1 (l n µ n )) t (4) However ths does not take nto account the msclassfcaton arsng due to degenerate moton explaned n prevous sectons. To overcome ths the eventual probablty s fused as a combnaton of eppolar and flow vector bound constrants as P n,s = α P EP,s + (1 α) P F V B,s where, α balances the weght of each constrant. A χ 2 test s performed to detect f the eppolar lne l n due to the mage pont s satsfyng the eppolar constrant. When Eppolar constrant s not satsfed, α takes a value close to 1 renderng the FVB probablty nconsequental. As the eppolar lne l n begns ndcatng a strong lkelhood of satsfyng eppolar constrant, the role of FVB constrant s gven more mportance, whch can help detect the degenerate cases. An analogous set of equatons characterze the probablty of an mage pont beng dynamc that are not delneated due to brevty of space. In our mplementaton, the envelope of eppolar lnes [14] s generated by a set of F matrces dstrbuted around the mean obtaned from of the R,t transformaton between two frames as estmated by the vsual SLAM. Hence a set of eppolar lnes correspondng to those matrces are generated and characterzed by the sample set, EL ss = (ˆl1, ˆl 2...ˆl ) q and the assocated probablty set, ( ) P EL = wˆl 1, wˆl 2...wˆl q where each wˆl j s the probablty of that lne belongng to the sample set EL ss computed through usual Gaussan procedures. Then the probablty that (3) (5) an mage pont p n s statc s gven by, P n,s = α j P S p EP,ˆlj n +(1 α j ) P S p F V B,ˆlj n wˆl j j=1 q (6) where, P EP,ˆlj S and P F V B,ˆlj S are the probabltes of the pont beng statonary due to the respectve constrants wth respect to the eppolar lne ˆl j. C. Clusterng Independent Motons Features wth hgh probabltes of beng dynamc are ether belongs to trackng outlers or potental movng objects. We adopt a smple move-n-unson model to cluster. Spatal proxmty and moton coherence s used to cluster these feature tracks nto ndependently movng enttes. By moton coherence, we use the heurstc that the varance n the dstance between features belongng to same object should change slowly n comparson. These features of spatal proxmty and moton coherence are then used n an agglomeratve clusterng framework to dvde the dynamc features nto movng enttes. D. Feedback to Vsual SLAM Features lyng over the ndependently movng objects are not used n the structure and moton estmaton by the vsual SLAM module. In spte of the use of robust estmators lke RANSAC [26], ndependently movng objects can gve rse to ncorrect ntal SfM estmate and lead the bundle adjustment to converge to a local mnma. The feedback also results n less number of outlers n the vsual SLAM process. Thus the structure and moton estmate s more well condtoned and less number of RANSAC teratons s needed [26]. Apart from mprovement n the camera moton estmate, the knowledge of the ndependent foreground objects comng from moton segmentaton helps n the data assocaton of the features, whch s currently beng occluded by that object. For the foreground ndependent motons, we form a convex-hull around the tracked ponts clustered as an ndependently movng entty. Exstng 3D ponts lyng nsde ths regon s marked as not vsble and s not searched for a match. Ths prevents 3D features from unnecessary deleton and rentalzaton, just because t was occluded by an ndependent moton for some tme. V. EXPERIMENTAL RESULTS The system s mplemented as threaded processes n C++ and runs n realtme at the average of 22Hz. The Independent moton detecton module takes around 10ms for each mage of 512x284 resoluton and wth 3 ndependently movng bodes. A. Results of Movng Object Detecton The system has been tested on a number of real mage datasets, wth varyng number and type of movng enttes. Movng object detecton results n the three sequences are dscussed next. Movng Box Sequence: Ths s same sequence as used n [16]. A prevously statc box s beng moved n front of the camera whch s also movng arbtrarly. However unlke [16],

5 Fg. 2. Eppolar lnes n Grey, flow vectors after rotaton compensaton s shown n orange. Cyan lnes show the dstance to eppolar lne. Features detected as ndependently movng are shown as red dots. Note the near-degenerate ndependent moton n the mddle and rght mage. However the use of FVB constrant enables effcent detecton of degenerate moton. our method does not uses any 3D model, and thus can work for any prevously unseen object. As shown n Fg. 3 our algorthm relably detects the movng object just on the bass of moton constrants. The dffculty wth ths sequence s that the foreground movng box s nearly whte and thus provdes very less features. Ths sequence also hghlghts the detecton of prevously statc movng objects. Fg. 4. Independent Moton detecton results from the New College Sequence. arbtrary camera trajectory, degenerate moton and changng number of movng enttes. C. Person detecton Fg. 3. Results from the Movng Box Sequence New College Sequence: We tested our results on some dynamc parts of the publcly avalable New College dataset [27]. Only left of the stereo mage pars has been used. In ths sequence, the camera moves along an roughly crcular campus path, and three movng persons passes by the scene. Fg. 4 depcts the moton segmentaton results for ths sequence. B. Detecton of Degenerate Motons Fg. 2 shows an example of degenerate moton detecton, as the flow vectors on the movng person almost move along eppolar lnes, but they are beng detected due to usage the FVB constrant. Ths results verfes system s performance for Some applcatons demand people to be explctly detected from other movng objects. We use part-based representatons [28], [29] for person detecton. The advantage of the part-based approach s that t reles on body parts and therefore t s much more robust to partal occlusons than the standard approach consderng the whole person. We model our mplementaton as descrbed n [28]. Haar-feature based cascade classfers was used to detect dfferent human body parts, namely upper body, lower body, full body and head and shoulders. These detectors often leads to many false alarms and mssed detectons. Bottom-left mage of Fg. 5 depcts the false detectons, by ths ndvdual detectors. A probablstc combnaton [28] of these ndvdual detectors gves a more robust person detector. But runnng four Haarlke-feature based detectors on the whole mage takes about 400ms, whch s very hgh for realtme mplementaton. We use knowledge of moton regons as detected by our method, to

6 reduce the search space of part detectors. Ths greatly reduces the computatons and the tme taken s mostly less than 40ms. Also the detectons have less false postves. Fg. 5. TOP LEFT: A scene nvolvng a movng toy car and person from the ndoor sequence. TOP RIGHT: Detected movng regons are overlad n green. BOTTOM LEFT: Haar classfer based body part detectors. BOTTOM RIGHT: Person detected by part-based person detecton over mage regons detected as movng. VI. CONCLUSIONS Ths paper presents a realtme movng object detecton algorthm from a sngle freely movng monocular camera. An onlne vsual SLAM algorthm runnng smultaneously estmates the camera egomoton. Multvew geometrc constrants were explored to successfully detect varous ndependent moton ncludng degenerate motons. A probablstc framework n the model of a recursve Bayes flter was developed that assgns probablty of a feature beng statonary or movng based on geometrc constrants. 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