Detection and tracking of ships using a stereo vision system

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1 Scientific Reseach and Essays Vol. 8(7), pp , 18 Febuay, 2013 Available online at DOI: /SRE ISSN Academic Jounals Full Length Reseach Pape Detection and tacking of ships using a steeo vision system Gazi Kocak 1,2 *, Shigehio Yamamoto 1 and Takeshi Hashimoto 3 1 Gaduate School of Maitime Sciences, Kobe Univesity, Japan. 2 Maitime Faculty, Istanbul Technical Univesity (ITU), Tuzla, Istanbul, Tukey. 3 Faculty of Engineeing, Shizuoka Univesity, Japan. Accepted 12 Febuay, 2013 Maitime tanspotation continues to maintain the lagest volume in the tanspotation secto, and this is inceasing day-by-day. This means thee is an eve-inceasing demand fo safe navigation. In this study, we popose a steeo vision system fo detecting and tacking ships as a tool fo inceasing navigational safety. Ships ae detected fom obtained images by using an edge detection algoithm that uses a moving aveage to find high-intensity gadients. The detected points ae clusteed and the 3D location of a detected ship is calculated fom a steeo image pai. The detected ship is then tacked fo seveal tens of seconds to obtain its couse. The esult of ou expeiment indicates that the poposed steeo vision system is useful as a tool fo safe navigation. Key wods: Safety navigation, ship detection, tacking, compute vision, steeo vision. INTRODUCTION Ocean-going vessels ae equipped with vaious electonic devices fo navigation, such as automatic ada plotting aid (ARPA) and automatic identification system (AIS). Howeve, existing devices ae not pefect, and the navigational abilities of ships ae esticted by these devices. In addition, the AIS is not mandatoy fo ships unde 300 GT, and small vessels cannot be detected by the AIS, which inceases the collision isk. The occuences of accidents at sea pove that existing navigational devices ae inadequate. Studies show that up to 75 to 96% of maitime accidents and casualties ae due to some fom of human eo (Peea et al., 2011). Moe specifically, an impotant cause of collision is an impope look-out being maintained by navigation offices, which accounts fo 86% of collisions (Shimpo et al., 2008). This makes the authoities and eseaches pay moe attention to incease the navigational safety. It is desiable to have many choices fo safety navigation which suppot the applicability of The Intenational *Coesponding autho. kocak_gazi@hotmail.com. Tel: Fax: Regulations fo Peventing Collisions at Sea (COLREGs) (IMO, 1972) in which the taget object location and couse ae essential infomation fo obstacle avoidance. Figue 1 illustates a cossing situation accoding to COLREGs ules. In the figue, V 0 and V 1 ae speeds of own vessel and taget vessel, espectively and X-Z is the coodinate system fo own vessel. As it can be seen fom the figue, the action fo the collision avoidance depends on the location, couse and the speed of the taget vessel. Recently some studies ae caied out fo the application of COLREGs in autonomous suface vehicles. These studies assume that the obstacles ae aleady detected and thei algoithm makes localization and mapping in accodance with COLREGs ules (Peea et al., 2011; Benjamin et al., 2006; Statheos et al., 2008). Howeve, the obstacle detection is a vey complex step fo collision avoidance which is the topic of this pape. Especially, automatic ship detection becomes moe impotant fo the safety navigation of ships. Automatic ship detection is epoted in some papes (Shimpo et al., 2005; Kiiya and Fukuto, 2005; Matins et al., 2007), which utilized digital images. Howeve, these studies use a single camea. In this case, it is vey difficult to obtain the location of a taget. The authos have peviously

2 Kocak et al. 289 Figue 1. Cossing (Own vessel gives way). In this study, futhe consideation on detection and localization is caied out and tacking of detected ships is included. The system can be used as a tool to aid navigation office s look-out and to complement othe electonic devices. The fundamental theoy of steeo vision has been explained in detail in many studies (Faugeas, 1993; Jain et al., 1995; Tucco and Vei, 1998; Hatley and Zisseman, 2003). The basic pinciple of steeo is that the pojection of a 3D object point has a unique pai of image locations in two cameas viewing the same scene fom diffeent viewpoints. These pojected points of the object ae called coesponding points. Theefoe, given two camea images, it is possible to calculate the 3D location of an object point by tiangulation, povided its image locations that coespond to the same physical point in the eal wold. One of the advantages in using steeo vision to detect a ship is that we can measue its 3D location. In addition, we can make use of the coespondence poblem mentioned above. That is, it is easy to find the coesponding points fo solid objects such as a ship o an obstacle between a pai of steeo images, while it is difficult fo moe unsteady things such as waves and eflections of the sea s suface. In this study, we popose a steeo vision system to help navigation offices by detecting othe ships and obstacles, measuing thei locations, and tacking them. The emainde of the pape is oganized as follows. Fistly, the detection and tacking algoithms - which include bilateal filteing, edge detection method, clusteing, 3D calculation and tacking - ae explained in detail. Then, basic expeimental esults ae pesented. Finally, the esults of the study ae summaized as conclusions. DETECTION AND TRACKING OF SHIPS FROM STEREO IMAGES Figue 2. Concept of the poposed system. poposed a new appoach fo the detection and localization of othe ships by means of a steeo vision system (Yamamoto and Win, 2006; Kocak et al., 2009a, b). Figue 2 shows the concept of the poposed system. A steeo camea system, which consists of two cameas set up on the staboad and the pot sides of a ship, detects othe ships o obstacles, measues thei 3D locations in the XYZ coodinate system fixed on the ship, and tacks them. The outline of the poposed method is shown in Figue 3. Fist, a pai of steeo images, which ae efeed to as left and ight images fo convenience, ae captued. Then, colo adjustment between the left and ight images is caied out fo a eliable coesponding point seach. Next, the vetical edges ae detected fom the efeence (left) image afte emoving the noise fom the image by using a smoothing filte. The coespondences of the vetical edges ae found in the ight image. Edges that cannot find a clea coespondence ae emoved. The emaining edges ae assumed to belong to the detected objects. We can detect ships and othe stuctues fom sea

3 290 Sci. Res. Essays R R, new, new ( Rl R ( Rl R, ave, ave R 255 R R R, ave, ave, ave )(255 R ), ave ) R ( R R ( R, ave R ), ave ) (1) Figue 3. Algoithm flowchat. images by using this pocedue. The detected points ae clusteed, which epesent the objects, and the locations of the objects ae measued by the pinciple of tiangulation, which is a obust technique fo calculating object locations. By epeating the above pocedues fo time seies images, we can tack the locations of objects and emove unsteady things such as waves. The following sections descibe these pocedues in detail. Pe-pocessing The images obtained fom the cameas ae RGB colo images that contain thee colo channels (Red, Geen, and Blue). Each colo channel has a value between 0 and 255. Afte captuing a pai of steeo images, colo adjustment between left and ight images is caied out fo a eliable coesponding point seach. A 3D scene point is viewed fom diffeent angles by the steeo cameas, which may esult in slightly diffeent colo values. Colo adjustment is caied out so that the mean value of each colo channel of the ight image agees with that of the left image. Fo example, the ed values of the ight image ae adjusted by whee R and R,new ae, espectively, the oiginal and adjusted ed values of the ight image; R l,ave and R,ave, espectively, ae the mean values of the ed channel of the left and the ight images. Sea images ae vey noisy because of the specula popety of the sea s suface. Theefoe, elimination of this noise though a smoothing filte is equied as a pepocess. Howeve, some edge data ae lost in the pocess of emoving the noise. Theefoe, we must choose a suitable smoothing filte. In this study, two types of smoothing filtes ae compaed. One of them is a 7 7 median filte and the othe is a bilateal filte. A median filte is a nonlinea filte that eliminates noise by taking the median of the soted intensity data. Howeve, some edge data ae also lost in this system. On the othe hand, the bilateal filte smoothes image noise while peseving the edges (Tomasi and Manduchi, 1998). The bilateal filte is a nonlinea filte though which the output image J s is the weighted aveage of the input image I, and the weighting fo each pixel p is detemined by the spatial distance and the intensity diffeence between the cente pixel s and pixel p within the window Ω. Theefoe, the value of pixel s is mainly affected by pixels that ae spatially close and have a simila intensity value. The output image is obtained by: J s p p f ( p s) g( I p f ( p s) g( I I ) I p s I ) s p whee the function f fo spatial domain and g fo intensity domain ae usually Gaussian functions. e 1 ps 2 ( ) 2 d f ( p s) (3) 1 I p I s 2 ( ) ( ) 2 e g I p I s (4) The esults of applying a median filte and a bilateal filte to a ship image ae shown in Figue 4. While the oiginal image is noisy, the filteed images ae smoothed. Howeve, in the median-filteed images, the edges of a ship hull ae deteioated, which makes detection difficult. In bilateal-filteed image, the shape of the ship is peseved, while the noise is emoved. Detection of salient points Points with high-intensity gadients ae assumed to be (2)

4 Figue 4. Effect Kocak et al. 291 (a) (b) (c) Figue 4. Effect of filteing on ship image a) Oiginal b) Median filteed c) Bilateal filteed. Figue 4. Effect Figue 5. Detection algoithm using the intensity pofile Figue 6. Coesponding point seach salient points and ae seached via the left steeo image. Although the images ae captued as colo images, we use the intensity o gayscale of the image to detect the salient points so as to save the computational cost. An intensity pofile of a gay channel on a scan line is shown in Figue 5. In this image, the solid line is the intensity values though hoizontal pixel locations, and the dashed line is the moving aveage of intensity values. The candidate salient points ae detemined as the intesection of moving aveage values and the intensity values (points A, B and C in Figue 5). Howeve, the intensity values ae vey unstable and esulting in too many candidate points. To ovecome this poblem, the aea fomed by the intesection of the intensity values and moving aveage line is consideed, which is shown as the blue aea in Figue 5. If the aeas befoe and afte the intesection points ae sufficiently lage, they ae detemined to be candidate points (points A and B in Figue 5), which indicate that the intensity change at this point is sufficiently high. The application of diffeent types of smoothing filtes affects the intensity pofile and hence the detection quality. The effects of a median filte and a bilateal filte on the intensity pofile and detection ae pesented in the expeimental esults pat of this wok. Finding coesponding points and dispaity Afte detecting salient points, coespondences fo these points ae seached in the ight image, as shown in Figue 6. Thee colo channels (R, G, and B) ae used fo steeo matching to incease the accuacy of the coesponding

5 292 Sci. Res. Essays Figue 7. Veification of matching. Figue 8. Sample coesponding point matching using the SSD similaity measuement point seach. Coesponding points ae calculated by the sum of squaed diffeences (SSD) method (Maghany et al., 2011) accoding to the following equation: R( d) ([ Rl ( i, j) R ( i d, j)] i, jw [ G ( i, j) G ( i d, j)] l 2 [ B ( i, j) B ( i d, j)] ) l 2 2 whee R, G and B ae RGB intensity values. The subscipts l and indicate the efeence (left) and obseved (ight) images, espectively. W is the aea (5) defined aound the candidate point in the efeence image. The pixel location d with the minimum value of R is detemined as the matching point. As mentioned ealie, sea images have a high-noise content, which esults in the detection of some false points as inteest points. Fo example, sea waves can be detected as object points. To confim the matching point to be tue, a evese-matching pocess is caied out, as shown in Figue 7. Hee the matched point in the ight image is seached in the left image and if both locations ae the same, this point is detemined to be a tue matching point. A sample coesponding point pai is shown in Figue 8.

6 Kocak et al. 293 Figue 9. Paabola fitting of similaities. Figue 10. Clusteing detected points. 3D measuement by steeo vision makes use of the dispaity between left and ight images to calculate the distance of an object. The dispaity deceased as the distance inceases, compaed to the baseline length, o the length between two cameas. Theefoe, we must identify the dispaity as coectly as possible and obtain it with sub-pixel accuacy, especially to measue a distant ship. The sub-pixel estimation is based on the similaity measues of thee pixel locations elated pixel, pevious pixel and next pixel calculated by the SSD. Afte calculating the SSD values, paabola fitting is caied out, the centeline location of which is the estimated sub-pixel position, as shown in Figue 9. The following equation is used fo the calculation of the paabola fitting: R( d 1) R( d 1) d sub (6) 2R( d 1) 4R( d) 2R( d 1) whee R(d-1), R(d), and R(d+1) ae the similaity measues of the pevious pixel, elated pixel and next pixel locations, espectively. Then, the dispaity is expessed by d + d sub. Clusteing detected object points When detection is completed, thee will be many points that belong to seveal objects. Fo bette object epesentation and tacking pefomance, the numbe of points epesenting the same object should be deceased as much as possible. Theefoe, the detected points ae clusteed by utilizing a steeo-clusteing algoithm that uses the pixel location and the dispaity data of a pai of steeo images. The points that ae at a cetain distance and simila dispaity values ae then collected in the same cluste. Each cluste is epesented by the aveage of point locations and dispaity values. When a new point is included in a cluste, the aveage of location and dispaity values ae updated, as shown in Figue 10. If the distance between the ed and geen points and the

7 294 Sci. Res. Essays the same pocedue as clusteing but extended though time seies images. The clustes of time t ae saved, and then detection is caied out in a new image at time t+1. Detected points in the new image ae included into a suitable existing cluste as detected points of time t. When a cluste is not updated seveal times in succession, it is emoved. This esults in tacking only stably detected clustes and eliminating unstable clustes though image sequences. The specula popety of the sea s suface causes some eflectance points on the sea suface to be peceived as an object point by a compute, and it is not easy to eliminate all noise despite smoothing the image by filteing. Combining detection and tacking algoithms solves this poblem, making it possible to confim detection by tacking though image sequences. The tacking of 3D locations of cetain object points will be stable and consistent, while the abitay sea suface eflectance will change in successive images. Theefoe, it is easy to eliminate such points by tacking them. In this algoithm, clusteing the detected points and tacking them though image sequences eliminate false detected points aising fom glae fom the sea s suface. Figue 11. () and () edges. dispaity is less than a cetain theshold value, the geen point will be included in the cluste and the aveage value will be efeshed, which epesents the enewal cluste. The distance theshold used in this study is 3 pixels fo the hoizontal diection and 10 pixels fo the vetical diection, while the dispaity theshold is 1 pixel. When a detected point cannot be included in any existing clustes, a new cluste is geneated. When the entie image is scanned, clustes that do not have a sufficient numbe of points fou in this study ae eliminated, and the emaining clustes ae saved to obtain the significant clustes. In this method, the detected edges of ships ae mostly accumulated at the bow and sten of the vessel. Theefoe, it is convenient to distinguish them. Classifying the clustes as () and () edges can help us achieve this. The concept of () and () edges is illustated in Figue 11. This is actually the concept of a bightness gadient. If the bightness is changing fom highe values to lowe values at the edge point, this edge is a () edge, othewise it is a () edge. Tacking by spatiotempoal clusteing and feedback to detection Afte clusteing the salient points of time t, these points ae tacked though image sequences. This is ealized by Measuement of a 3D location of an object The last step is the calculation of a 3D location (X, Y, and Z) using the dispaity. In the case of a standad steeo system in which the optical axes of both cameas ae paallel, the 3D location of an object is expessed by: X LB Y d d Z sub ( xl x y F ) / 2 whee (x l, y) and (x, y) ae the image coodinates of the object in the left and ight images, espectively. L B is the baseline length of two cameas, and F is the focal length of the cameas. The tiangulation pocess used to calculate the 3D locations is explained in detail in a pevious study by the authos (Yamamoto and Win, 2006). EXPERIMENTAL RESULTS Expeimental configuation An expeiment was caied out in the Akashi Stait in which we attempted to detect ships fom actual sea images. To detemine the basic pefomance of the algoithm, cameas wee set up on land not onboad a ship. The authos evaluated two diffeent expeimental configuations using diffeent camea types. They ae efeed as Configuation 1 and Configuation 2. (7)

8 is 10.1m, and the image esolution is 1920 x 1080 pixels. Kocak et al. 295 Figue 12. Expeimental camea setup and obtained sample images. Figue 12. Expeimental camea setup and obtained sample images Configuation 1 uses two identical 3CCD Panasonic NV- GS300 video cameas with a zoom lens. The view angle of the cameas is a maximum 43 and the image esolution is pixels. The cameas ae set up nealy paallel to the camea axis with an 8.14m baseline length. A tipod is used fo the calibation puposes. Figue 12 shows the camea setup and sample steeo images. In Configuation 2, two identical high-esolution cameas, Canon EOS 5DIIs, ae used. The baseline length between the cameas in this case is 10.1m, and the image esolution is pixels. Detection Fist, the pefomance of salient point detection is ascetained using Configuation 1. One scan line (327 th ) of a bilateal-filteed image is displayed in Figue 13 with diffeent moving aveage values to show some of the local effects of the algoithm. The blue line is the intensity pofile of one scan line. The ed, geen, and puple lines ae moving aveage values with 71 pixels, 35 pixels, and 29 pixels, espectively. A 71-pixel moving aveage is shown to be bette fo the detection pupose fom the figue because it foms geate aeas at highe-intensity gadients. Theefoe, 71-pixel moving aveage data ae used in the est of the study, and aeas geate than 30 pixels ae consideed to be inteest points. The intensity values of a scan line ae changed afte the application of smoothing filte, which affects detection quality. Scan lines afte the application of a median filte and a bilateal filte ae shown in Figue 14. The figue shows a bidge pie and two ships. One of the ships is close and has a clea image, while the othe is fathe out and not so clea. The impotant point fo detection is shown within the black cicle, which the median-filteed image cannot detect the salient point, while the bilatealfilteed image can. In addition, the median filte tuncates the shap edges, while the bilateal filte peseves them. The detected salient points ae shown in Figue 15. It is clea fom the figue that the bilateal filte impoves the detection quality. The positive effect of bilateal filteing can be moe clealy obseved in the tacking data. Clusteing In Figue 16, the esult of clusteing detected points in

9 Intensity Intensity 296 Sci. Res. Essays Image Intensity MA_71 MA_35 MA_ Hoizontal pixel location. In addition, Figue 13. Intensity the median pofile of image filte values tuncates with diffeent the moving shap aveage edges, values. while the bilateal filte peseves them. Hoizontal pixel location Figue 14. Gay Figue channel 14. Gay intensity channel pofile intensity of pofile a sea of a image. sea image. Top: Top: Nonfilteed, Middle: Nonfilteed, Median Middle: filteed, Median filteed, Bottom: Bilateal Filteed.

10 Z [m] The detected salient points ae shown in Figue 15. It is clea fom the figue that the bilateal filte impoves the detection quality. The positive effect of bilateal Kocak filteing et al. 297 can be moe clealy obseved in the tacking data. (a) (b) Figue 15. Detected points in left image afte (a) median filte (b) bilateal filte Clusteing Figue 15. Detected points in left image afte (a) median filte (b) bilateal filte. illumination conditions and fo diffeent ships in Configuation 2 ae displayed i Figue Ship 1 Ship 2 Bidge Pie X [m] Figue 16. Clusteing esult of detected points. Figue 15 can be obseved. The clustes ae mainly located at the bow and sten of the ship. Figue 17. Calculated 3D locations of clustes. Figue 17. Calculated 3D locations of clustes In Figue 17, the calculated 3D locations of detected clustes ae displayed in the X-Z dimensions. The detection and clusteing esults of vaious situations

11 298 Sci. Res. Essays Figue 18. Detection and clusteing esults unde vaious conditions. unde diffeent illumination conditions and fo diffeent ships in Configuation 2 ae displayed in Figue 18. We attempted to detemine the actual position of the ships by using a suveying equipment (Topcon GPT- 7500). Howeve, this was not possible because the ships wee in motion. Theefoe, a stable point on the bidge is measued to show the accuacy of the 3D measuement of the steeo camea system. The pojections of this point to the left and ight images ae shown in Figue 19. The 3D location of this point measued by the suveying equipment is ( m, m, m) which ae the X, Y, and Z distances, espectively. 3D measuement of this bidge point is pefomed by the steeo camea system fo 50 images though image sequences in Configuation 2. The aveage of these 50 calculations is ( m, m, m). A

12 Kocak et al. 299 Figue 19. The bidge point measued fo 3D accuacy Figue 19. The bidge point measued fo 3D accuacy. images that ae equivalent to tacking of 30 seconds. Ship1 Ship2 Ship1 Ship2 Ship1 Ship2 Bidge Pie Figue 20. 3D locations of tacked points fo median-filteed images Figue 20. 3D locations of tacked points fo median-filteed images. The application of a bilateal filte impoves clusteing and tacking, esulting in bette detection. The tacking esult of the same image sequence compaison of the 3D measuement esult of the suveying equipment and steeo camea system shows that the accuacy of the 3D measuement of the steeo camea system is acceptable. Tacking by spatiotempoal clusteing These points, which epesent one cluste, ae tacked though the sequences of images, and the 3D locations ae calculated fo each image. Figue 20 shows the calculated locations of all tacked cluste points fo median-filteed images fo 300 images that ae equivalent to tacking of 30 s. The application of a bilateal filte impoves clusteing and tacking, esulting in bette detection. The tacking esult of the same image sequence, shown in Figue 20, afte application of a bilateal filte is shown in Figue 21. Compaing the esults of tacking obtained in Figue 21 with that obtained in Figue 20, we can obseve an

13 300 Sci. Res. Essays the couse of the ship. This is vey impotant fo collision avoidance puposes. Ship1 Ship2 Bidge Pie Figue 21 3D locations of tacked points fo bilateal filteed images Figue 21. 3D locations of tacked points fo bilateal filteed images. Fom the tacking esults in Figue impovement in tacking though image sequences. The locations of clustes ae not as scatteed as shown in Figue 20. They fom a moe line-like figue, which shows the couse of the ship. This is vey impotant fo collision avoidance puposes. Fom the tacking esults in Figue 21, we can visually constitute the elationship between tacked clustes and figue out that thee ae two ships moving though the X axis, which is shown by the aow. The tacking esults of diffeence image sequences in Configuation 2 using a bilateal filte ae shown in Figue 22. When the detected points ae classified as () and () accoding to edge detection, the esult will be as displayed in Figue 23. Fom this figue, it is obseved that tacked clustes which seem to be one pat in Figue 21 can be split into two pats. These epesent the detected points in the sten and bow of the ship. while the distance of Ship 2 is aound 1400 m. Howeve, thee ae also some falsely detected points whose locations have significantly changed. While the false detected points ae at aound 380 and 1400 m, in the following image, thei locations have changed to 900 m and 3900 m, espectively. By combining the detection and tacking algoithm, we eliminate such false detections. Clusteing the detected points and combining detection by tacking give some satisfactoy esults. We can confim the detection by tacking though image sequences. While a ship is stably detected evey time, the sea waves ae not. Futhemoe, we impoved ou detection algoithm by localizing detected points. The detected points belonging to a ship fom a egula cluste. Howeve, the localizing of wave points shows an iegulaity, which enables us to detemine that these points ae false detections. Combining detection and tacking Moving to the next image in image sequences and detecting the salient points, we obseve that cetain object points, such as a ship o bidge pie, ae stably detected, and thei locations also emain consistent values. This is shown in Figue 24. Fo example, the distance of Ship 1 is aound 1200 m in both images, CONCLUSION This pape descibed a method to detect, measue, and tack ships fom sea images using steeo vision fo the pupose of safe navigation. The algoithm used in the method is as follows: 1. Detect the point at which the intensity of the image

14 Z [m] diffeence image sequences in Configuation 2 using a bilateal filte ae shown in Figue 22. Kocak et al. 301 Ship1 Ship2 Ship1 Ship2 Ship2 Ship1 Bidge Pie Ship2 Ship1 Ship2 Ship3 Ship1 Ship3 Ship2 Ship1 Bidge Pie Figue 22. Tacking esults with diffeent image sequences Figue 22. Tacking esults with diffeent image sequences. pats. These epesent the detected points in the sten and bow of the ship (-) (+) X [m] Figue 23. Tacking Figue 23. data Tacking of (+) and data (-) edges. of () and () edges Combining Detection and

15 302 Sci. Res. Essays 3. Measue the 3D location of the detected points. 4. Cluste the points. 5. Tack the clustes though image sequences. False Detections (b) Image at time (t) The meit of using steeo vision is not only that it can measue the 3D locations of objects, but also it is useful fo the detection of the objects. That is, a coesponding poblem between a pai of steeo images can distinguish clea objects, such as ships, fom unclea objects, such as waves. In addition, tacking the objects makes the distinction cleae because ships can continuously be detected though image sequences. Ou expeimental esults indicate the potential of the poposed method, although thee ae some points to impove. Fist, the accuacy of the 3D measuement is sometimes insufficient, as it heavily depends on the pecise calibation of a steeo camea system. That is, we must obtain intinsic paametes such as the focal length and lens distotions of the cameas and extinsic paametes, which include the locations and oientations of the cameas. Futhemoe, we must cope with the pitching and olling of the ship and vibation to the cameas because the system is used onboad the dynamic envionment of a ship. The authos ae cuently studying a method to pecisely measue camea oientation by means of the hoizon and seveal standad points that ae chosen onboad the ship (Kocak et al., 2012). Second, detected points ae solely clusteed and tacked by closeness of coodinates and dispaity in the image in this pape. The authos ae consideing the use of a Kalman filte o paticle filte to estimate the movement of the ship. This will enable calculation of a ship s couse and velocity. ACKNOWLEDGEMENT This wok was suppoted by Gant-in-Aid fo Scientific Reseach (C), KAKENHI ( ), fom JSPS. The authos ae thankful to M. Yasuhio Nomua fo his suppot. REFERENCES (a) Image at time (t +1) Figue Changing Changing positions positions of abitay of abitay false points. false points significantly changes in the hoizontal diection as salient points. 2. Find the coesponding points of the salient points between a pai of steeo images. Benjamin RM, Cucio J, Leonad JJ, Newman PM (2006). A Method fo Potocol-Based Collision Avoidance Between Autonomous Maine Suface Caft. J. Field Robot. 23(5): Faugeas O (1993). Thee-Dimensional Compute Vision. The MIT Pess. Hatley R, Zisseman A (2003) Multiple View Geomety in Compute Vision. Cambidge Univesity Pess. Intenational Maitime Oganization (IMO) Web page of Conventions, aspx. Jain R, Katsui R, Schunck BG (1995). Machine Vision. McGaw-Hill Pess. Kiiya N, Fukuto J (2005). Recognition technique of moving objects by intefame diffeential method using maine obsevation imagey. J. Japan Inst. Navigation 113:

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