!!!"#$%!&'()*&+,'-%%./01"&', Tokihiko Akita. AISIN SEIKI Co., Ltd. Parking Space Detection with Motion Stereo Camera applying Viterbi algorithm

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2 !!!"#$%!&'()*&+,'-%%./01"&', Tokihiko Akita AISIN SEIKI Co., Ltd. Parking Space Detection with Motion Stereo Camera applying Viterbi algorithm!

3 !"#$%&'(&)'*+%*+, -. /"0123'4*5 6.&/",70&"$2'37+89&:&;7+%3#7&<"+8&'<+797="+7'* A.&BC<%379%*+"$&3%,4$+, D.&E499"3F "

4 #$%&'()*+, Automated parking is spreading Parking spaces are narrow in some places Accurate detection of vehicle shape is necessary Camera has higher space resolution than sonar Utilize installed Camera surround view camera > Sonar Motion stereo Monitor./)*(% ) ,))( :;<=(>8%70 -

5 Conventional: Feature-based motion stereo Principle Trend ;)8+>2%7)*, Area-based motion stereo (Direct Method) 3D point reconstruction (point cloud) from corresponding feature points for parking Estimation Optimize disparity in all spaces Input image 3D Dense map dense point ;()%00,8+'42)N2P?/2Q)(7,2M)+'( K80++$62R)E2=;LSSTUV2W!ST!XE R0YM)IA0620>E2=7E2ZG?=H12G0+402?($%&8+'2$+,2H$558+'28+230$7L?8I0F62PMMKTT B

6 Optimize Total Variation (TV) constraint " E ( u) =! SAD( p, u) +!%( p#, u# ) $ ( p, u) %(, ) (, ) e G p! u! p u = u " u! # $ p p#" L p ;0+$7>O2>0(I2N)(2,845$(8>O29$(8$>8)+ ^+0('O2_2/8I87$(8>O2A0>Y00+28I$'025$>%@042`2,845$(8>O29$(8$>8)+ E(u) : energy at disparity u SAD(p, u) : a value of sum of absolute difference (SAD) at disparity u on image pixel p! : a TV constraint term varied by gradient G in image patch I0 " : an adjustment parameter The penalty is imposed on all disparity change between p and p with disparity u belonging to neighbor L p. Estimate optimal disparity û by minimizing the following energy function uˆ = arg min E( u) " arg min! e( p, u) e u u allviterbi paths H8+8I8]020+0('O28+2Y@)7028I$'02W$7725$>@4X ( p u) min e( p " 1, u! ) + %( ) ( ) SAD( p, u) { }, = p" 1, u! # p, u + u!$ Lu /)7902AO2(0%*((0+%02N)(I*7$2*>878]8+'2GO+$I8%2;()'($II8+' Lu : every disparity node connected from neighbor pixel p-1 to a node of current pixel and disparity (p, u) Disparity u e(p-2, u-2) e(p-2, u-1) e(p-2, u) e(p-2, u+1) Pixels p e(p-1, u-2) e(p-1, u-1) Viterbi nodes e(p, u-2) e(p, u-1) e(p-1, u) e(p, u) e(p-1, u+1) e(p, u+1) [

7 (a1) Horizontal structure (a2) (a3) (b1) Mirror surface (b2) (d1) Occlusion (e1) Almost horizontal boundary (c1) No texture area Normalized original image Factor (a) Horizontal structure (b) Mirror surface (c) No texture area (d) Occlusion (e) Almost horizontal boundary (a4) Abstract Cause matching error by repeating similar patterns in the horizontal direction Detect reflected image in the far area on mirror surface Fill in the area with few texture so that disparity change is not occurred No correct matching point is appeared by changing the view point Corresponding position error is occurred for slightly-inclined horizontal line due to the slight camera parameter error (e2) (c2) Disparity image and the problems Front Back white black V

8 1. Calculation exclusion for area with low reliability of disparity (1) Absolute value evaluation.!"#$ %& ' ( ) * + "#$, - %& &/0 If qstd qstd1, then skip disparity calculation for the image patch area I ij : intensity of a pixel at image coordinates i, j N : size of image patch (N N) i, j : horizontal and vertical image coordinates in image patch stdh( ) : standard deviation function for a horizontal line in image patch (i=1,, N) (2) Relative value evaluation ^b%7*,02,845$(8>o2%$7%*7$>8)+2)n28i$'025$>%@2$(0$ Y8>@24I$7724>$+,$(,2,098$>8)+28+2>@02@)(8])+>$72,8(0%>8)+ Ex. ^b%7*,02,845$(8>o2%$7%*7$>8)+2)n28i$'025$>%@2$(0$4 Y8>@248I87$(25$>>0(+2Y@8%@2)N>0+2$550$( If count % ( 2! CntHS, skip disparity calculation for the image patch area count( ) : function which calculates number of satisfying condition in ( ) SAD(p, u i ) : similarity function (SAD) of pixel p and disparity u i SAD1 : threshold of similarity (SAD) I : depth of disparity (i=1,, maximum calculation disparity) CntHS : threshold of frequency of similar disparity Ex. a

9 2. Induction into disparity with high reliability in energy function E ' ( u) = SAD( p, u) + ' ( ) ( ) + Sp( p, u) % p = " % p!$ L " G ( p!, u! )#( p, u ) & e u u Sp n ( p, u) = ( C # SAD( p, u + i) )! i=" n SAD(p, u) C i n i! SAD value p p!, u! # p, u % p : SAD value (degree of similarity) at disparity u : filter coefficients : length of filter coefficient "added energy term Reduce energy at the disparity where similarity of the pattern is uniquely high Similarity of the pattern is uniquely high Energy of Sp p SAD value Energy of Sp p Examples of energy term Sp Similarity of the pattern is not uniquely high U

10 Start Calculate disparity by Viterbi algorithm Filter disparity noise Calculate 3D point cloud Generate occupancy grid map Search free space Extract boundary cells between free space and obstacles Disparity map Extract cells fitting vehicle shape model (orthogonal line segments) Extract outermost point cloud in the cells Calculate orthogonal line segment by least square method Extract point cloud at the corners from cells near the intersection Extract outermost points by calculating convex hull Calculate ellipse model contacting the orthogonal line segments by non-linear optimization method End TS

11 Start Calculate disparity by Viterbi algorithm Filter disparity noise Calculate 3D point cloud Generate occupancy grid map Search free space Extract boundary cells between free space and obstacles Extract cells fitting vehicle shape model (orthogonal line segments) Extract outermost point cloud in the cells Point cloud in 3D sapce Calculate orthogonal line segment by least square method Extract point cloud at the corners from cells near the intersection Extract outermost points by calculating convex hull Calculate ellipse model contacting the orthogonal line segments by non-linear optimization method End TT

12 Start Calculate disparity by Viterbi algorithm Filter disparity noise Calculate 3D point cloud Generate occupancy grid map Search free space Extract boundary cells between free space and obstacles Extract cells fitting vehicle shape model (orthogonal line segments) Free space Extract outermost point cloud in the cells Calculate orthogonal line segment by least square method OGM and free space Extract point cloud at the corners from cells near the intersection Extract outermost points by calculating convex hull Calculate ellipse model contacting the orthogonal line segments by non-linear optimization method End T!

13 Start Calculate disparity by Viterbi algorithm Filter disparity noise Calculate 3D point cloud Generate occupancy grid map Search free space Extract boundary cells between free space and obstacles Extract cells fitting vehicle shape model (orthogonal line segments) Extract outermost point cloud in the cells Calculate orthogonal line segment by least square method Extract point cloud at the corners from cells near the intersection Vehicle shape cells Extract outermost points by calculating convex hull Calculate ellipse model contacting the orthogonal line segments by non-linear optimization method End T"

14 Start Calculate disparity by Viterbi algorithm Filter disparity noise Calculate 3D point cloud Generate occupancy grid map Search free space Extract boundary cells between free space and obstacles Extract cells fitting vehicle shape model (orthogonal line segments) Extract outermost point cloud in the cells Calculate orthogonal line segment by least square method Extract point cloud at the corners from cells near the intersection Extract outermost points by calculating convex hull Calculate ellipse model contacting the orthogonal line segments by non-linear optimization method Point cloud at edge Orthogonal fitting lines Point cloud at edge Vehicle shape orthogonal lines End T-

15 Start Calculate disparity by Viterbi algorithm Filter disparity noise Calculate 3D point cloud Generate occupancy grid map Search free space Extract boundary cells between free space and obstacles Extract cells fitting vehicle shape model (orthogonal line segments) Extract outermost point cloud in the cells Calculate orthogonal line segment by least square method Extract point cloud at the corners from cells near the intersection Extract outermost points by calculating convex hull Calculate ellipse model contacting the orthogonal line segments by non-linear optimization method Point cloud at the corners Convex hull Ellipse center Ellipse Vehicle shape outlines with orthogonal lines and ellipses End TB

16 d(8'8+$728+5*>28i$'04 Sequentially captured fisheye camera images for perpendicular parking scene D:\ITS Project\Projects\02_FundamentalImageRecognition\ DenseStereo\Evaluation\OriginalImage\ \Le ftsidecamera bmp TSS2I40% bmp Previous image Latest image *The camera is mounted under the left side mirror T[

17 R)(I$78]0,28I$'042N)(25()%0448+' Superimpose Normalized images Superimpose Superimposed image (parallax) TV

18 "L,8I0+48)+$72(0%)+4>(*%>8)+25)8+>4 Point cloud in far area Point cloud on objects Bird s eye view Point cloud on road Top view Front view Ta

19 "L,8I0+48)+$72(0%)+4>(*%>8)+25)8+>42WI)980X TU

20 Point cloud Free space Outline Outline Optical center of camera Top view!s

21 (For conventional Semi-Global Matching) Disparity map by conventional SGM Disparity map by proposed method white: near black: far Point cloud by conventional SGM is swelled and uneven 3D reconstruction points (blue: conventional SGM, red: proposed method)!t

22 (For VisualSFM+CMPMVS) Disparity map by comparison software Disparity map by proposed method red near blue far *Using sequential 8 images Reconstruct densely, however large errors appear Surface image with texture by comparison software 3D-reconstruction points *VisualSFM+CMPMVS: Free software which is the best one in this field and becomes a standard for comparison!!

23 d(8'8+$728+5*>28i$'04 Sequentially captured fisheye camera images for parallel parking scene D:\ITS Project\Projects\02_FundamentalImageRecognition\ DenseStereo\Evaluation\OriginalImage\ \Le ftsidecamera bmp TSS2I40% bmp Previous image Latest image *The camera is mounted under the left side mirror!"

24 R)(I$78]0,28I$'042N)(25()%0448+' Superimpose Normalized images Superimpose Superimposed image (parallax)!-

25 "L,8I0+48)+$72(0%)+4>(*%>8)+25)8+>4 Point cloud in far area Point cloud on objects Point cloud on road Bird s eye view Top view Front view!b

26 "L,8I0+48)+$72(0%)+4>(*%>8)+25)8+>42WI)980X![

27 Point cloud Curb area Outline Free space Optical center of camera Top view!v

28 (For VisualSFM+CMPMVS) Disparity map by comparison software Disparity map by proposed method red near blue far *Using sequential 8 images Reconstruct densely, however large errors appear *Reason: adjacent vehicles appear only in the part of all 8 images Surface image with texture by comparison software 3D-reconstruction points *VisualSFM+CMPMVS: Free software which is the best one in this field and becomes a standard for comparison!a

29 M)+%7*48)+! ;()5)40240I8L,0+402I)>8)+24>0(0)24O4>0I )N2,845$(8>O! c*>*(02y)(& 4*(N$%02AO2$,)5>8+'2$550$($+%02$+,240I$+>8%2&+)Y70,'0!U

30 H0(%82,029)>(0 "S

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