A Graduation Thesis of College of Engineering, Chubu University Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata
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1 A Graduation Thesis of College of Engineering, Chubu University Pose Estimation by Regression Analysis with Depth Information Yoshiki Agata
2
3 CG [2] [3][4] 3 3 [1] HOG HOG TOF(Time Of Flight) iii
4 1 TOF iv
5 1 TOF TOF SDK Histograms of oriented gradients(hog) Poser opengl v
6 vi
7 1.1 TOF (SR-4000) TOF V=1.0 HSV HSV HOG Poser vii
8 viii
9 4.1 Recall ix
10
11 1 TOF TOF TOF LED 1.1 mesa Swiss Ranger SR TOF 10MHz 1 LED TOF 1
12 1 TOF 1.1: TOF (SR-4000) 2
13 1.2: TOF 3
14 1 TOF 1.1 TOF SDK TOF TOF SDK(Software Development Kit) SDK TOF SDK API(Application Program Interface) SDK API typedef struct CMesaDevice SRCAM SR-4000 int SR OpenUSB( SRCAM srcam, unsigned int serialnumber ) SR-4000 int SR Close( SRCAM srcam ) SR-4000 int SR Acquire( SRCAM srcam ) unsigned int SR GetCols(SRCAM srcam) unsigned int SR GetRows(SRCAM srcam) ( ) int SR CoordTrfFlt( SRCAM srcam, float* x, float* y, float* z, int pitchx, int pitchy, int pitchz ) (x y z ) 1.2 TOF 0m 5.0m (z ) HSV 1.3 HSV 1.4 4
15 : V=1.0 HSV 1.4: HSV 5
16 1 TOF 1.3 TOF SR-4000 SR [1] TOF 1.5[2] 2 SR : 6
17 : 1.4 TOF 5m TOF 4 5m : 7
18
19
20 2 2.2 [1] [5] HOG HOG HOG 3 HOG TOF 2.3 HOG [7] HOG Histograms of oriented gradients(hog) [1] HOG HOG [6] ( 2.1 (b)) HOG I(u, v) m(u, v) θ(u, v) (2.1) (2.2)
21 2.3. m(u, v) = I u (u, v) 2 + I v (u, v) 2 (2.1) θ(u, v) = tan 1 I v(u, v) I u (u, v) I u (u, v) = I(u + 1, v) I(u 1, v) I v (u, v) = I(u, v + 1) I(u, v 1) (2.2) (2.3) 2.1: HOG m θ (2.4) c (p p ) θ v c (θ ) = u m(u, v)δ[θ, θ(u, v)] (2.4) v δ Kronecker θ θ 1 (2.4) θ N V c = {v c (1), v c (2),, v c (N)} ( 2.1 (d)) θ c 8 8 θ 8 (2.5) (q q ) 2 2 v c(n) = v c (n) q q N v c (k) 2 + ɛ k=1 (ɛ = 1) (2.5) 11
22 2 1 V c V c = {v c(1), v c(2),, v c(b N)} B [7] (2.6) Bhattacharyya m S = pu q u (2.6) u= : 12
23 3 3 2 TOF : 13
24 D Poser : Poser 3D Poser Poser
25 : Poser Poser opengl Poser opengl α aspect x c y c z c φ x φ y φ z h m θ x w y w z w T bg T in 15
26 3 3.4: z c
27 : 3.6: 17
28 depth map (x, y, z) = TOF : 3 18
29 : 3 19
30 3 3.9: 3.10: 20
31 C (a) (b) 3.11: 3.12: 21
32 [pixel] : 3.14: 22
33 x = (x 1, x 2,..., x 496 ) 57 y = (y 1, y 2,..., y 57 ) n 496 n X = (x 1, x 2,..., x n) T 57 n Y = (y 1, y 2,..., y n) T X Y (3.1) A A = arg min A AX Y 2 (3.1) A TOF X Y Y (3.2) ε Y = A X + ε (3.2) 23
34
35 4 4.1 Recall( ) 1 0 true positive total pixel 4.1 A B true positive total pixel 4.1 Recall Recall 1 Recall = true positive total pixel (4.1) 25
36 4 4.1: [ ] HOG
37 : [ ] 10[ ] 27
38 4 HOG 4.3 Recall 60[ ] 4.3: 28
39 HOG (D-HOG) HOG (HOG) (DDF) Recall Recall Recall : 29
40 4 4.5: 4.1: Recall DDF D-HOG HOG WAVE WALK HOG D-HOG
41 : 31
42 4 4.7: 32
43 : 33
44
45 recall HOG 0.13 D-HOG 0.09 HOG 0.17 D-HOG
46
47 37
48
49 [1],,,, HOG 3,MIRU2008,pp ,2008. [2],,,,, MIRU2006, pp.70-77, [3],, tree-based fil- tering, MIRU2006, pp.63-69,2006. [4] M. Andriluka, S. Roth, and B. Schiele, Pictorial structures revisited: People detection and artic- ulated pose estimation CVPR,pp ,2009. [5],,,, 3 MIRU2010 pp [6] N. Dalal and B, Triggs, Histograms of Oriented Gradients for Human Detection, IEEE Computer Vision and Pattern Recognition, pp , [7],,,SSII2009,,
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2 Fig D human model. 1 Fig. 1 The flow of proposed method )9)10) 2.2 3)4)7) 5)11)12)13)14) TOF 1 3 TOF 3 2 c 2011 Information
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ONLINE_MANUAL
JPN ii iii iv v 6 vi vii viii 1 CHAPTER 1-1 1 2 1-2 1 2 3 4 5 1-3 6 7 1-4 2 CHAPTER 2-1 2-2 2-3 1 2 3 4 5 2-4 6 7 8 2-5 9 10 2-6 11 2-7 1 2 2-8 3 (A) 4 5 6 2-9 1 2-10 2 3 2-11 4 5 2-12 1 2 2-13 3 4 5
ONLINE_MANUAL
JPN ii iii iv v vi 6 vii viii 1 CHAPTER 1-1 1 2 1-2 1 2 3 1-3 4 5 6 7 1-4 2 CHAPTER 2-1 2-2 2-3 1 2 3 4 5 2-4 6 7 8 2-5 9 10 2-6 11 2-7 1 2 2-8 3 (A) 4 5 6 2-9 1 2-10 2 3 2-11 4 5 2-12 1 2 2-13 3 4 5
Step2 入門
ii iii iv v vi NEC Corporation 1999 vii C O N T E N T S PART 1 PART 2 PART 3 viii PART 4 ix C O N T E N T S PART 5 x PART 6 xi C O N T E N T S PART 7 xii PART 8 PART 9 xiii C O N T E N T S xiv xv PART
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4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q
![4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q](/thumbs/92/110416683.jpg "4. C i k = 2 k-means C 1 i, C 2 i 5. C i x i p [ f(θ i ; x) = (2π) p 2 Vi 1 2 exp (x µ ] i) t V 1 i (x µ i ) 2 BIC BIC = 2 log L( ˆθ i ; x i C i ) + q")
x-means 1 2 2 x-means, x-means k-means Bayesian Information Criterion BIC Watershed x-means Moving Object Extraction Using the Number of Clusters Determined by X-means Clustering Naoki Kubo, 1 Kousuke
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リファレンス
STEP1 STEP 2 STEP 3 ii iii iv v NEC Corporation 1998 vi C O N T E N T S P A R T 1 viii ix C O N T E N T S P A R T 2 x P A R T 3 xi C O N T E N T S P A R T 4 xii P A R T 5 xiii C O N T E N T S P A R T
vi アハ ート2 アハ ート3 アハ ート4 アハ ート5 アハ ート6 アハ ート7 アハ ート8 アハ ート9 アハ ート10 アハ ート11 アハ ート12 アハ ート13 アハ ート14 アハ ート15 アハ ート16 アハ ート17 アハ ート18 アハ ート19 アハ ート20 アハ
iii vi アハ ート2 アハ ート3 アハ ート4 アハ ート5 アハ ート6 アハ ート7 アハ ート8 アハ ート9 アハ ート10 アハ ート11 アハ ート12 アハ ート13 アハ ート14 アハ ート15 アハ ート16 アハ ート17 アハ ート18 アハ ート19 アハ ート20 アハ ート21 アハ ート22 アハ ート23 vii アハ ート 24 アハ ート 25 アハ ート26
3 A 18 11 19 2 1 JIA 2 (JSCA) 3 4 5 6 7 8 9 10 11 12 13 14 B 4 I. (1) 18 3 29 30 33 12 18 3 542,264 22.5 62 46.3 242 149 29% 3 7 7 1 2 1 2 0.1.1 0.1.2 60 17 1 3 6 8 1 2 0.1.3 1 2 0.1.4 5 (2) (1) 0.2.2
1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +
![1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +](/thumbs/81/83701592.jpg "1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +")
3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows
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imageWARE Prepress Manager Select エディターガイド
Disc Disc Disc Disc Disc Disc Disc ii iii iv v vi vii viii ix 1 x xi xii 1 CHAPTER 1-1 1-2 1 2 1-3 1-4 1-5 1 2 1-6 1 2 1-7 1 2 3 1 2 3 1-8 1 2 1 2 1-9 1 2 1 2 1-10 1 1 1-11 1 1 1 2 1-12 1 1 2 1 2 1-13
01_SWGuide_V8.50.fm
ii iii iv v 2 vi vii viii ix x xi xii xiii xiv xv xvi xvii 1 CHAPTER 1-1 1-2 1-3 2 CHAPTER 2-1 2-2 2-3 2-4 1 2 2-5 3 4 2-6 5 6 2-7 7 8 2-8 9 2-9 10 11 2-10 12 13 2-11 14 15 2-12 16 17 18 2-13 1 2 2-14
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OFISTAR H7000ファクス編（2版 ）
i ii A iii A A A A A A A A A B C D A A B C D iv B A AB A A B A B A v A C B A ABC A B C A A vi A B B A A A A A A vii A A viii ix 4 5 6 8 9 0 x xi 5 6 9 6 06 C 4 5 8 6 9 0 C 06 06 06 064 065 066 06 068 069