Multi-Target Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs)

Transcription

1 2016 IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems (IROS) Daejeon Convenion Cener Ocober 9-14, 2016, Daejeon, Korea Muli-Targe Deecion and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs) Jing Li 1, Dong Hye Ye 1, Timohy Chung 2, Mahias Kolsch 2, Juan Wachs 1 and Charles Bouman 1 Absrac Despie he recen fligh conrol regulaions, Unmanned Aerial Vehicles (UAVs) are sill gaining populariy in civilian and miliary applicaions, as much as for personal use. Such emerging ineres is pushing he developmen of effecive collision avoidance sysems. Such sysems play a criical role UAVs operaions especially in a crowded airspace seing. Because of cos and weigh limiaions associaed wih UAVs payload, camera based echnologies are he de-faco choice for collision avoidance navigaion sysems. This requires muliarge deecion and racking algorihms from a video, which can be run on board efficienly. While here has been a grea deal of research on objec deecion and racking from a saionary camera, few have aemped o deec and rack small UAVs from a moving camera. In his paper, we presen a new approach o deec and rack UAVs from a single camera mouned on a differen UAV. Iniially, we esimae background moions via a perspecive ransformaion model and hen idenify disincive poins in he background subraced image. We find spaio-emporal rais of each moving objec hrough opical flow maching and hen classify hose candidae arges based on heir moion paerns compared wih he background. The performance is boosed hrough Kalman filer racking. This resuls in emporal consisency among he candidae deecions. The algorihm was validaed on video daases aken from a UAV. Resuls show ha our algorihm can effecively deec and rack small UAVs wih limied compuing resources. I. INTRODUCTION Increasing affordabiliy, funcionaliy and versailiy are leading o a realiy where unmanned aerial vehicles (UAVs) are pervasive in he sky for commercial and individual needs [1], [2], [3]. One of he mos imporan challenges associaed wih UAV s use is collision avoidance or senseand-avoid capabiliy [4]. As opposed o large auonomous vehicles where mos of he collision avoidance is done by LIDAR sensors, UAV have a limied payload o be effecive. Inexpensive opical sensors such as Go-Pro color cameras enable low energy consumpion, are ligh, and mos suiable for use on UAVs. These provide a cos effecive alernaive o sysems currenly in use on larger aircrafs, like raffic collision avoidance sysem (TCAS). Machine vision based collision avoidance sysems require he deecion and racking of oher UAVs from real-ime video feeds o be usable [5], [6]. Once oher UAVs are 1 Jing Li, Dong Hye Ye, Juan Wachs and Charles Bouman are wih he Deparmen of Elecrical and Compuer Engineering and Indusrial Engineering, Purdue Universiy, Wes Lafayee, IN 47907, USA {li1463,yed,jpwachs,bouman}@purdue.edu 2 Timohy Chung and Mahias Kolsch are wih he Deparmen of Sysem Engineering and Compuer Science, Naval Posgraduae School, Monerey, CA 93943, USA {hchung,kolsch}@nps.edu *This work was suppored by he Naval Posgraduae School Gran NPS- BAA Fig. 1. Challenge in deecing oher UAVs: [Lef] Original Video, [Righ] Video wih our deecion and racking; Oher UAVs are very small and occluded by complex backgrounds (i.e. cloud) and hus no even recognizable by human eyes. Our proposed mehod deecs and racks muliple small UAVs successfully as highlighed in red boxes. deeced and racked, sraegies involving a sequence of maneuvers for collision avoidance are followed. For realime operaion, deecion and racking operaions mus run on-board. This allows coninuous operaions even when he connecion beween he drone and he conrol saion is los, or sensors fail. In his conex, real-ime objec deecion and racking has been he subjec of sudy by he compuer vision communiy in large [7], [8]. Specific challenges associaed wih objec deecion and racking algorihms applied o UAV applicaions involve he following: (1) The video feed is acquired from a moving camera mouned on he UAV, which requires o sabilize he rapidly changing views (ofen requiring non-planar geomery based ransformaions); (2) Due o he speed of UAVs in converging orienaions, he moving objecs need o be deeced in a far disance o enable imely warning before collision. The arges appear small in image frame and ofen occluded by cluer (e.g. clouds, rees, and specular ligh) (See Fig. 1 Lef). To ackle hese challenges, a new approach is presened in his paper o deec and rack small UAVs from a rapidly moving camera mouned on a UAV. Iniially, he acquired video from he camera is parsed ino a sequence of frames and he relaive background moion beween frames is esimaed. The guiding assumpion is ha UAVs and he background have very differen moion paerns. Thus, he moving objec can be exraced by compensaing he moion of he background. The background moion is calculaed via he perspecive ransform model [9] aking ino accoun globally smooh moion wih camera projecion. The moving objecs salien poins are idenified from a background subraced image. The local moion of he moving objecs is deermined by applying he Lucas-Kanade opical flow algorihm [10]. Candidae objecs (arges) are found by classifying spaio /16/$ IEEE 4992

2 emporal feaures from he esimaed local moion of he moving objec. Finally, he Kalman filer racking [11] is applied o reduce he inermien miss-deecions and false alarms found hrough he camera feed. This algorihm was esed on real videos from UAVs and arge UAVs were deeced even when hey were no visible due o heir size and he background complexiy (See Fig. 1 Righ). II. RELATED WORK Previous approaches o deec and rack moving objecs using camera-based sysems mouned on UAVs [12] rely mainly on he exracion of salien feaures in individual frames and use machine learning echniques o prooype he shape and appearance of he arge objecs in he raining daase [13]. Classifiers including Convoluional Neural Neworks (CNN) [14] and Random Foress (RF) [15] o menion a few, have been used for his ask, and achieved robus performance even in challenging environmens (e.g. variable illuminaion and background cluer). However, in mos cases, he auhors assume sufficienly large and clearly visible moving objecs wih disinguishable shape and appearance feaures. This assumpion does no hold for warning sysems since hey need o aler early enough o avoid he imminen oherwise collision. A differen approach relies sricly on moion informaion from he moving objec for furher deecion and racking. Such approach is suiable for characerizing small moving objecs since heir moions can be esimaed in local regions beween frames. Moion-based approaches are divided ino wo main caegories: (1) Background Subracion based and (2) Opical Flow based. Background subracion mehods idenify groups of pixels which brighness remain consan over ime and hen subrac hose pixels from he image o deec he moving objecs [7], [16]. These background subracion based mehods work bes when background moion can be easily compensaed, which is no he case for a fas moving camera. Alernaively, opical flow based mehods find corresponding image regions beween frames. Then, based on he local moion vecors, he moving objecs are deeced [10], [17]. The qualiy of local moion vecors is criical for accurae deecion. Blurred images can lead o poor moion vecor assessmens. We propose o combine background subracion and opical flow mehods o obain he bes of boh worlds. We use background moion esimaion for he moving camera as a firs approximaion, and hen subrac mos of homogeneous regions in he image o isolae he arge objecs. These consiue salien regions in he background subraced image enable o find good poins for opical flow maching. More imporanly, by comparing background moion and flow vecor based approaches, we can exrac spaio-emporal feaures which have shown o be useful for moving objec deecion and racking. III. MULTI-TARGET DETECTION AND TRACKING As illusraed in Fig. 2, we propose an efficien muliarge deecion and racking algorihm for UAVs. We firs Video Background Mo-on Es-ma-on Background- Subraced Image Moving Objec Deec-on Candidae Objecs Targe Classifica-on & Tracking UAV Mask Fig. 2. An overview of our proposed mehod: We firs esimae he background moion beween wo sequenial frames. From resuling backgroundsubraced image, we deec he moving objecs by pruning spurious noise. Among deeced objecs, we differeniae UAVs from false alarms using spaio-emporal characerisics and rack hem for emporal consisency. esimae he background moion beween wo sequenial video frames and subrac he background o highligh he regions where changes have occurred. Addiional moving objec deecion operaions are performed o differeniae arge objecs from spurious noise. Finally, we use spaioemporal characerisics of each deeced objec o idenify acual UAV and incorporae he emporal consisency of deeced objecs hrough racking. In following, we describe each componen of our algorihm in deails. A. Background Moion Esimaion For background moion esimaion, we assume ha he background moves smoohly, no allowing local warping. From a sequence of video frames, we exrac a se of poins and esimae local moion fields on hose seleced poins. The local moion esimaion procedure can be compuaionally expensive, so i is only performed on a sparse se of seleced poins based on saliency wih appropriaely uniform disribuion. The compued local moion fields are hen fi ino a global ransformaion which represens he background moion. 1) Idenify Salien Poins: Firs, we idenify salien poins in a video frame. Here, we use Shi-Tomasi corner deecor [18] due o efficiency. Shi-Tomasi corner deecor is based on he assumpion ha corners are associaed wih he local auocorrelaion funcion. Given an image X, we define he local auocorrelaion funcion C a he pixel s as following: C(s) = [X(s + δs) X(s)] 2 (1) W where δs represens a shif and W is a window around s. The shifed image X(s + δs) is approximaed by a firsorder Taylor expansion and hen eq. (1) can be rewrien as following: C(s) = [ X(s) δs] 2 W = δs T [ X(s) T X(s)]δs W = δs T Λδs where X is he firs order derivaive of he image and Λ is he precision marix. In Shi-Tomasi corner deecion, a saliency Q is compued according o eigenvalues of Λ. (2) Q(s) = min{λ 1,λ 2 } (3) 4993

3 where λ 1 and λ 2 are eigenvalues of Λ. Afer hresholding on Q, we find a se of salien poins. To ensure appropriaely uniform spaial disribuion, we discard poins for which here is a sronger corner poins a a cerain disance. 2) Find Local Moion Fields on Salien Poins: We now find he local moion fields from he previous frame X 1 o he curren frame X on idenified salien poins. We denoe p 1 as one of Shi-Tomasi corner poins in X 1. Then, we compue he moion vecor u from he poin p 1 using Lucas-Kanade mehod [10] assuming ha our local moion is opical flow. In Lucas-Kanade mehod, all neighbor poins around he given pixel should have he same moion. So, he local moion can be compued by solving he leas square problem. u = argmin u s N (p 1 ) X (s + u) X 1 (s) 2 (4) where N (p 1 ) is he neighborhood around p 1. I is worh noing ha eq. 4 is easy o solve wih a closed-form soluion. Furhermore, we use bi-direcional verificaion o obain accurae moion vecor such ha u + (u ) 1 2 has small value. 3) Fi Local Moion Fields o a Global Transformaion: Afer finding a se of local moion fields u, we fi hem ino a global ransformaion. We now denoe p = p 1 + u as he corresponding poin in he curren frame X hrough opical-flow maching. We hen find he global ransformaion H which regularizes local moion fields o be smooh in he enire image. H = argmin H p P,p 1 P 1 p H p (5) where P and P 1 represen a se of corresponding poins in X and X 1, respecively, and is he warping operaion. Then, here are many widely-used global ransformaion models such as rigid or affine ransformaion model. Here, we choose he perspecive ransformaion model [9] reflecing he fac ha a UAV occupy a small porion of he field of view. The perspecive ransformaion model is efficien o compue because i requires only 9 parameers o describe and i can ake accoun ino projecion based on he disance from he camera. We assign he resuling perspecive ransformaion in eq. 5 as he background moion beween wo consecuive frames. B. Moving Objec Deecion Given he esimaed background moion, we compue he background subraced image o highligh moving objecs which have more complex moion. Then, we idenify he salien poins in he background subraced image and use appearance informaion o find he local moion vecor on hose poins. The addiional es is performed o prune spurious noise assuming ha moion of arge objecs is largely differen from he background moion. 1) Compue Background Subraced Image: We can subrac he background by aking difference beween original image and background moion compensaed image. However, he esimaed background moion may no be accurae as single plane assumpion on he perspecive model can be violaed in a video. Therefore, we use he background moions esimaed from muliple previous frames o obain more accurae background subraced image. We now denoe H 1 as he perspecive ransform beween wo previous frames from X 2 o X 1. Then, we compue he background subraced image E 1 for X 1 by aking average of forward and backward racing. E 1 = 1 2 X 1 H 1 X X 1 (H ) 1 X (6) where (H ) 1 is he inverse ransform of H. I is worh noing ha we compue he background subraced image for he previous frame X 1 for symmery. 2) Find Salien Poins on Moving Objecs: The moving objecs are highlighed in he background subraced image E 1. Then, we need o find he corresponding regions in X o deec moving objecs in he curren frame. Toward his, we firs exrac Shi-Tomasi corner poins in E 1 (refer Secion III-A.1) and propagae hem o appearance image X 1. For each propagaed corner poin from X 1, we find he corresponding poin in X by applying Lucas-Kanade mehod (refer Secion III-A.2). We now denoe q 1 as he corner poin in X 1 propagaed from E 1. Then, he local moion field v is compued as following: v = argmin v s N (q 1 ) X (s + v) X 1 (s) 2 (7) where N (q 1 ) is he neighborhood around q 1. I is worh noing ha we do no use he background subraced image bu he appearance image o esimae he local moion. 3) Prune Salien Poins based on Moion Difference: We now have he corresponding poins in X from E 1, which can be used o deec moving objecs. However, we may have poins on spurious noise (i.e. edge of background) due o incorrec background moion esimaion. Therefore, we prune he poins according o he difference beween he esimaed background and local moion. This is based on he assumpion ha arge objec has very differen moion compared wih background. We now define he moion difference d beween he background and moving objec as following: d = h v (8) where h is inerpolaed moion vecor from he perspecive ransform H a he poin q 1. We hen find he pruned poin r according o he magniude of moion difference. r = q 1 + v if d 2 > T (9) where T is he empirical hreshold for pruning. By applying conneced componen labeling [19] on he se of pruned poins, we can cluser hem according o 4994

4 spaial proximiy. We generae he bounding box for each cluser of poins which represens our deecion for a single moving objec. C. Targe Classificaion and Tracking While we expec our moving objec deecion o be effecive, we sill encouner false alarms among he deeced objecs. Therefore, we obain a se of spaio-emporal feaures for each deeced objec and deermine wheher he objec is our arge or no. In addiion, in order o preven inermien miss-deecion and false alarm, we apply a racking echnique enforcing coheren emporal signaures of deecion. 1) Classify Targe Objecs: Given he deeced objecs, we perform classificaion o rejec ouliers from rue arges. We now denoe R (n) as a cluser of poins o represen he n h objec in X. Then, we compue he wo feaures which encode spaio-emporal characerisics of he objec. The firs feaure characerizes he coherency of moion difference vecors in R (n). Here, we assume ha he arge is non-deformable objec, and hus he moion vecors on he arge objec are consisen. We now define he feaure f (n) as he angle variance of moion difference vecors. f (n) = µ (n) θ = d D (n) d D (n) arcand µ (n) θ 2 S (n) arcand S (n), (10) where D (n) is he se of moion difference vecors for R (n) and S (n) is he number of poins in R (n). The second feaure characerizes he spaial disribuions of poins in he objec. Here, we assume ha here are densely disribued salien poins for he arge objec. The feaure g (n) is hen defined as he poin densiy in R (n). g (n) = S(n) B (n) (11) where B (n) is he area of minimum bounding box ha encloses all poins in R (n). Given hese feaures, we build a classifier o idenify arge objecs. We denoe y (n) as a classificaion label where he posiive value indicaes ha n h objec is he arge. Then, he arge classifier is defined as following: y (n) = { 1, if f (n) < T 1 and g (n) > T 2 1, oherwise (12) where T 1 and T 2 are he empirical hresholds for angle variance and poin densiy in R (n). 2) Track Targe Objecs: Even hough our arge classifier reduces he false alarms, we also expec o have inermien miss-deecions and false alarms. These inermien missdeecions and false alarms can be correced by observing he emporal characerisics of he deeced objecs. Therefore, we apply racking echniques o enforce coheren emporal signaures of deeced objecs. Specifically, we use he Kalman filer [11] for objec racking. Kalman filer predics he curren sae b from previously esimaed saes ˆb 1 wih ransiion model and updaes he curren measuremen c wih he curren sae b as below: b = Aˆb 1 + ω c = Mb + ε (13) where A is sae ransiion marix, ω conrols he ransiion modeling error, M is measuremen marix, and ε represens he measuremen error. The esimaed oupu ˆb is hen compued wih Kalman gain K: ˆb = Aˆb 1 + K(c Mb ) K = V ω M T (MR ω M T +V ε ) (14) where V ω and V ε are he covariance of ω and ε, separaely. In our applicaion, we assign he size and locaion of bounding box for he deeced objec as sae variable b and use he consan velociy model o se A and M. To iniialize he Kalman filer, we find he corresponding objecs from opical flow maching in L previous frames and sar rack if he classificaion labels y (n) 1,,y(n) L are consisen. Then, we recover he miss-deecion for he posiive label rack and delee he false alarm of he negaive label rack based on he Kalman filer oupu a he curren frame. We dismiss he rack if we do no have deeced objecs in he Kalman filer esimaion for L frames. IV. EXPERIMENTS We evaluae our muli-arge deecion and racking algorihm for UAVs on a video daa se provided by Naval Posgraduae School. The videos are aken in oudoor environmen including real-world challenges such as illuminaion variaion, background cluer, and small arge objecs. A. Experimenal Seup 1) Daa Se: The daa se comprises 5 video sequences of 1829 frames wih 30 fps frame rae. They are recorded by a GoPro 3 camera (HD resoluion: or ) mouned on a cusom dela-wing airframe. As a preprocessing, we mask ou he pio ube region which is no moving in he videos. For each video, here are muliple arge UAVs (up o 4) which have various appearances and shapes. We manually annoae he arges in he videos by using VATIC sofware [21] o generae ground-ruh daase for performance evaluaion. 4995

5 Time Fig. 3. Resuls of muli-arge deecion and racking algorihms for four consecuive frames: [Top-Row] Background subracion mehod [20], [Middle-Row] Our mehod wih arge classificaion only, [Boom-Row] Our mehod wih arge classificaion and racking; Green boxes represen he deeced objecs. Background subracion mehod (Top-Row) deecs false alarms on he complex background (i.e. building). By using he arge classifier (Middle-Row), we rejec false alarms bu miss he deecion on arge UAVs occluded by background. Targe racking (Boom-Row) enforces emporal consisency of our deecion recovering inermien miss-deecion. Images are zoomed for beer display. See full images in supplemenary files. 2) Parameer Exploraion: There are imporan parameers in our muli-arge deecion and racking algorihm. To begin wih, we exrac Shi-Tomasi corner poins in original image X 1 (for background moion esimaion in III-A.1) and background subraced image E 1 (for moving objec deecion in III-B.2), respecively. We se higher saliency hreshold (QE = 0.15) for E 1 han ha (QX = 0.001) for X 1 as we find he sparser se of poins in he background subraced image where only moving objecs should be idenified. In addiion, we use block size for LucasKanade opical flow maching (Secion III-A.2 and III-B.2). Nex, we se he hreshold T = 1.8 o prune he poins wih large moion difference (Secion III-B.3) and he hresholds T1 = 5 and T2 = 0.02 for arge classifier wih angle variance and poin densiy feaures (Secion III-C.1). Finally, we use L = 6 for Kalman filer where we sar he rack if we deec he objec in six previous frames (Secion III-C.2). B. Quaniaive Evaluaion The overall goal of his experimen is o measure he deecion accuracy of idenifying arges in videos. We also analyze he compuaional ime as our algorihm needs o be run on board efficienly. TABLE I D ETECTION ACCURACY Background Subracion [20] Targe Classificaion Only (Ours) Targe Classificaion / Tracking (Ours) F ) Deecion Accuracy: To measure deecion accuracy, we repor F-score which is he harmonic mean of recall and precision raes compued as following: Recall = Number of Deeced Targes in all Frames Number of Ground-Truh Targes in all Frames Precision = Number of Deeced Targes in all Frames Number of Deeced Objecs in all Frames 2 Recall Precision Recall + Precision Here, we define he deeced arge if our deecion has overlap wih ground ruh. We compare our proposed mehod wih he sae-of-he ar background subracion mehod [20] which was developed for pedesrian deecion wih a saic camera. We also repor deecion accuracy only wih our arge classificaion o highligh he imporance of racking. Table I summarizes he accuracy scores. The background subracion mehod shows low F-score. This is because fas moving background in he video from UAV causes many false alarms. Our mehod significanly improves F-score indicaing ha our arge classifier based on background and moion difference correcly idenify arge objecs. By using our arge racking, F-score was furher improved due o reduced inermien miss-deecions and false alarms. 2) Compuaional Time: We run our algorihm on a sandard 3.5GHz clock rae Inel processor deskop wih 8GB memory. We implemen single-hreaded Pyhon codes wih OpenCV library. The average compuaional ime for each frame is ± 23.36ms. The main compuaional boleneck is o compue he background subraced image since applying a global ransformaion o he large HD 4996 F=

6 and false alarms. Experimenal resuls on acual videos aken from UAVs show ha he proposed mehod can achieve high accuracy scores in erms of recall and precision raes and be efficien o run on board for collision avoidance sysem. Fig. 4. Shi-Tomasi corner poins deeced from background subraced images in wo videos: Red and green dos represen pruned and deleed poins based on he magniude of moion difference vecor, respecively. We preserve he poins around arge UAV (red), while deleing he poins locaed a corner of building srucures (green). This indicaes ha he magniude of difference vecor beween esimaed background and local moion is effecive o separae he poins in he arges from complex backgrounds. Images are cropped for beer display. image ( or ) is a heavy compuaional burden. By down-sampling he video by facor of 2 and using muli-hread implemenaion, our algorihm is efficien enough o run on board near real-ime. C. Visual Inspecion We complemen he quaniaive evaluaion above wih qualiaive visual inspecion. Fig. 3 shows exemplar resuls from our mehod wih arge classifier only and wih racking. For reference, we also illusrae he resul wih background subracion mehod [20]. We noice ha background subracion mehod generaes false alarms on complex backgrounds such as buildings. Our mehod rejecs mos false alarms hanks o arge classifier based on moion difference bu misses he deecion on arges due o background cluer. Our Kalman filer racking significanly improves he deecion on arges by enforcing emporal consisency of he deecion. In Fig. 4, we display he Shi-Tomasi corner poins deeced from background subraced images. We use differen colors for preserved (red) and deleed (green) poins by applying hresholding on he magniude of moion difference. We observe ha preserved and deleed poins are mosly locaed around he arge UAV and building srucures, respecively. This reflecs ha we supplemen he background subracion by pruning he poins based on moion difference, improving he deecion accuracy. V. CONCLUSIONS In his paper, we proposed a muli-arge deecion and racking algorihm for UAVs. Our mehod firs esimaes he background moion from a fas moving camera via perspecive ransformaion model. We hen find he sparse se of salien poins from background moion compensaed image and esimae local moion on hose poins hrough opical flow maching. By comparing he difference beween background and local moions, we idenify candidae moving objecs and classify wheher each moving objec is arge or no. We furher refine he deecion using emporal informaion from Kalman filer, reducing inermien miss-deecions REFERENCES [1] P. S. Lin, L. Hagen, K. Valavanis, and H. Zhou, Vision of unmanned aerial vehicle (UAV) based raffic managemen for incidens and emergencies, in World Congress on Inelligen Transpor Sysems, [2] M. Neri, A. Campi, R. Suffrii, F. Grimaccia, P. Sinogas, O. Guye, C. Papin, T. Michalareas, L. Gazdag, and I. Rakkolainen, SkyMedia - UAV-based capuring of HD/3D conen wih WSN augmenaion for immersive media experiences, in IEEE Inernaional Conference on Mulimedia and Expo, [3] F. Nex and F. Remondino, UAV for 3D mapping applicaions: a review, Applied Geomaics, vol. 6, no. 1, pp. 1 15, [4] D. Xing, D. Xu, and F. Liu, Collision Deecion for Blocking Cylindrical Objecs, in IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems, [5] C. Hane, C. Zach, J. Lim, A. Ranganahan, and M. Pollefeys, Sereo Deph Map Fusion for Robo Navigaion, in IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems, [6] S. Roelofsen, D. Gille, and A. Marinoli, Reciprocal collision avoidance for quadroors using on-board visual deecion, in IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems, [7] N. Oliver, B. Rosario, and A. Penland, A Bayesian Compuer Vision Sysem for Modeling Human Ineracions, IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 22, no. 8, pp , [8] L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, PIXHAWK: A Sysem for Auonomous Fligh Using Onboard Compuer Vision, in IEEE Inernaional Conference on Roboics and Auomaion, [9] I. Carlbom and J. Paciorek, Planar Geomeric Projecions and Viewing Transformaions, ACM Compuing Surveys, vol. 10, no. 4, pp , [10] B. Lucas and T. Kanade, An Ieraive Image Regisraion Technique wih an Applicaion o Sereo Vision, in Inernaional Join Conference on Arificial Inelligence, [11] G. Welch and G. Bishop, An Inroducion o he Kalman Filer, Universiy of Norh Carolina a Chapel Hill, Tech. Rep., [12] A. Rozansev, V. Lepei, and P. Fua, Flying Objecs Deecion from a Single Moving Camera, in IEEE Conference on Compuer Vision and Paern Recogniion, [13] P. Dollar, Z. Tu, P. Perona, and S. Belongie, Inegral Channel Feaures, in Briish Machine Vision Conference, [14] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, Robus Objec Recogniion wih Corex-Like Mechanisms, IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 29, no. 3, pp , [15] A. Bosch, A. Zisserman, and X. Munoz, Image Classificaion Using Random Foress and Ferns, in Inernaional Conference on Compuer Vision, [16] N. Seungjong and J. Moongu, A New Framework for Background Subracion Using Muliple Cues, in Asian Conference on Compuer Vision, [17] T. Brox and J. Malik, Large Displacemen Opical Flow: Descripor Maching in Variaional Moion Esimaion, IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 33, no. 3, pp , [18] J. Shi and C. Tomasi, Good feaures o rack, in IEEE Conference on Compuer Vision and Paern Recogniion, [19] H. Same and M. Tamminen, Efficien Componen Labeling of Images of Arbirary Dimension Represened by Linear Binrees, IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 10, no. 4, pp , [20] Z.Zivkovic and F. der Heijden, Efficien adapive densiy esimaion per image pixel for he ask of background subracion, Paern Recogniion Leers, vol. 27, no. 7, pp , [21] C. Vondrick, D. Paerson, and D. Ramanan, Efficienly Scaling up Crowdsourced Video Annoaion, Inernaional Journal of Compuer Vision, vol. 101, no. 1, pp ,