2007/8 Vol. J90 D No. 8 AdaBoos Haar-like AdaBoos Viola Jones Haar-like [17] (1) Haar-like AdaBoos (2) Suppor Vecor Tracking SVT [1] SVT [6] Okuma [10
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1 a) 3D People Tracking Using he Paricle Filer wih Cascaded Classifiers Yoshinori KOBAYASHI a),daisukesugimura,kousukehirasawa, Naohiko SUZUKI,HiroshiKAGE,YoichiSATO, and Akihiro SUGIMOTO Haar-like AdaBoos AdaBoos 1. Insiue of Indusrial Science, The Universiy of Tokyo, Komaba, Meguro-ku, Tokyo, Japan Advanced Technology R&D Cener, Misubishi Elecric Co., Tsukaguchi-honmachi, Amagasaki-shi, Japan Naional Insiue of Informaics, Hiosubashi, Chiyoda-ku, Tokyo, Japan a) yosinori@iis.u-okyo.ac.jp [2], [4] [16], [18], [19] [2], [4], [5], [7], [9], [11] [14], [16], [18] D Vol. J90 D No. 8 pp c
2 2007/8 Vol. J90 D No. 8 AdaBoos Haar-like AdaBoos Viola Jones Haar-like [17] (1) Haar-like AdaBoos (2) Suppor Vecor Tracking SVT [1] SVT [6] Okuma [10] Okuma Yang [19] Coarse-o-Fine 2 Thierry AdaBoos [15] [7], [18] [14] Nickel [8] x z Z = {z 1,...,z } P (x Z 1) 1 P (x 1 Z 1) 1 P (x x 1) 2050
3 P (x Z 1) = P (x x 1)P (x 1 Z 1)dx 1. (1) P (z Z 1) P (x Z ) P (z x ) P (x Z 1) P (x Z ) P (z x )P (x Z 1). (2) P (x Z ) P (x Z ) x {s (1),...,s (N) } {π (1),...,π (N) } 1 1 P (x 1 Z 1) N {s (1) 1,...,s(N) 1 } {π (1) 1,...,π(N) 1 } {s (1) 1,..., s (N) 1 } 2 {s (1) 1,...,s (N) 1 } P (x x 1 = s (n) 1 ) P (x Z 1) N {s (1),..., s (N) } 3 π (n) {s (1),...,s (N) } π (n) P (z x = s (n) ) 3. Viola Jones [17] 1(a) 1(a) H i H i(x) 1(b) h (x) (a) Cascade (b) Feaures 1 Fig. 1 Cascaded classifer. ( T ) H i(x) =sgn α h (x). (3) =1 T α ɛ α =log 1 ɛ ɛ AdaBoos XY Z XY Z (x, y, z) Z θ 4. 2 P (x x 1) [ s (n) 1 = x (n) 1,y (n) 1,z (n) 1 1],θ (n) 2051
4 2007/8 Vol. J90 D No. 8 s (n) s (n) = s (n) 1 + υ + ω. (4) υ ẋ ẏ ż θ ω 0 Σ ω Σ ω σx σ 2 y σ 2 z σ 2 θ 2 s (n) 4. 3 [ ] n s (n) = x (n),y (n),z (n),θ (n) i F i ( ) p (n) = Fi s (n). (5) p (n) s(n) i i θ (n) θ (n) = θ (n) [ an 1 [ C i Ks (n) C i Ks (n) ] y ] x. (6) C i i XY K s (n) XY [] x X i l i p (n) θ (n) li s (n) 4. 4 g (n) h (x) g (n) g (n) g (n) g (n) g (n) g (n) n s (n) π (n) n s (n) i p (n) θ (n) l(n) 2 s (n) p (n) l (n) g (n) 4 θ (n) θ (n)
5 g (n) g (n) π (n) π (n) π (n) = π (n). (7) i (7) (a) (b) 2 Fig. 2 Deecion of head posiion. (a) #450 (b) # IEEE Poin Grey Research Flea PC Peium4 3.2 GHz Memory 1 GBye υ ẋ ẏ ż θ 10 Σ ω σx σ 2 y σ 2 z σ 2 θ 2 σ x =4cm σ y =4cm σ z =2cm σ θ = cm 2 (c) #600 (d) #650 3 Fig. 3 Tracking resuls m 2m ms 1.2 cm 6.6 cm ms 2 4 XY XZ Z XY 2053
![2007/8 Vol. J90 D No. 8 5 Fig. 5 Muliple people racking. 4 Fig. 4 Trajecory of a user s head posiion. 1 Table 1 Tracking error. [cm] [cm] Z 1.02 0.65 XY 1.98 1.](/docs-images/93/113664333/images/6-0.jpg "46 1 XY Z 2cm 4 1 5. 2 5m 5m 3 1 50 ms 5.1 cm 16.5 cm 2.8 7.1 5 5 20 20 1 XY XZ 6 4 Z XY 2 XY 5cm 1 5cm Vermaak [16] Vermaak 1 Vermaak [16] 6. 6. 1 1 1cm 7(a) 1 (3) 7(b) 2054")
6 2007/8 Vol. J90 D No. 8 5 Fig. 5 Muliple people racking. 4 Fig. 4 Trajecory of a user s head posiion. 1 Table 1 Tracking error. [cm] [cm] Z XY XY Z 2cm m 5m ms 5.1 cm 16.5 cm XY XZ 6 4 Z XY 2 XY 5cm 1 5cm Vermaak [16] Vermaak 1 Vermaak [16] cm 7(a) 1 (3) 7(b) 2054
![2 Table 2 Tracking errors. [cm] [cm] A Z 2.59 1.73 XY 5.08 3.29 B Z 2.14 1.60 XY 5.63 3.99 C Z 3.77 2.29 XY 5.03 3.](/docs-images/93/113664333/images/7-0.jpg "08 (a) A (a) (b) B (b) (c) Fig. 7 7 Likelihood disribuion. (c) C 6 Fig. 6 Trajecories of users head posiion.")
7 2 Table 2 Tracking errors. [cm] [cm] A Z XY B Z XY C Z XY (a) A (a) (b) B (b) (c) Fig. 7 7 Likelihood disribuion. (c) C 6 Fig. 6 Trajecories of users head posiion. 7(c) 7(a) 7(b) 2055
8 2007/8 Vol. J90 D No. 8 Fig. 9 9 Relaion of likelihood o head direcion. 8 Fig. 8 Rejec samples in each sage. 7 7(b) cm XY Z
9 Fig Accuracy comparison of single and muliple classifier racking. Fig Accuracy comparison of 2 and 3 camera racking XY Z (a) 12 (b) 2057
![2007/8 Vol. J90 D No. 8 (a) (b) 12 Fig. 12 Robusness of muli-camera racking. 7. Helmu [3] [1] S. Avidan, Suppor vecor racking, IEEE Trans. Paern Anal. Mach. Inell., vol.26, no.8, pp.1064 1072, 2004.](/docs-images/93/113664333/images/10-0.jpg "[2] S. Birchfield, Ellipical head racking using inensiy gradiens and color hisograms, Proc. IEEE Inernaional Conference on Compuer Vision and Paern Recogniion, pp.232 237, 1998. [3] G. Helmu, G.")
10 2007/8 Vol. J90 D No. 8 (a) (b) 12 Fig. 12 Robusness of muli-camera racking. 7. Helmu [3] [1] S. Avidan, Suppor vecor racking, IEEE Trans. Paern Anal. Mach. Inell., vol.26, no.8, pp , [2] S. Birchfield, Ellipical head racking using inensiy gradiens and color hisograms, Proc. IEEE Inernaional Conference on Compuer Vision and Paern Recogniion, pp , [3] G. Helmu, G. Michael, and B. Hors, Real-ime racking via on-line boosing, Proc. Briish Machine Vision Conference, vol.1, pp.47 56, [4] M. Isard and A. Blake, Condensaion Condiional densiy propagaion for visual racking, In. J. Compu. Vis., vol.29, no.1, pp.5 28, [5] G. Loy, L. Flecher, N. Aposoloff, and A. Zelinsky, An adapive fusion archiecure for arge racking, Proc. 5h IEEE Inernaional Conference on Auomaic Face and Gesure Recogniion, pp , [6] vol.46, no.sig CVIM 11, pp.60 71, [7] Condensaion MIRU2006 pp , [8] K. Nickel, T. Gehrig, R. Siefelhagen, and J. McDonough, A join paricle filer for audiovisual speaker racking, Proc. 7h Inernaional Conference on Mulimodal Inerfaces, pp.61 68, [9] K. Nummiaro, E. Koller-Meier, and L. Van Gool, An adapive color-based paricle filer, Image Vis. Compu., vol.21, no.1, pp , [10] K. Okuma, A. Taleghani, N. Freias, J. Lile, and D. Lowe, A boosed paricle filer: Muliarge deecion and racking, European Conference on Compuer Vision, vol.3021 of LNCS, pp.28 39, [11] P. Prez, J. Vermaak, and A. Blake, Daa fusion for visual racking wih paricles, Proc. IEEE, vol.92, no.3, pp , [12] J. Sherrah and S. Gong, Fusion of percepual cues for robus racking of head pose and posiion, Paern Recogni., vol.34, no.8, pp , [13] vol.43, no.sig 2058
![CVIM 4, pp.69 84, 2002. [14] D-II vol.j88-d-ii, no.8, pp.1592 1600, Aug. 2005.](/docs-images/93/113664333/images/11-0.jpg "[15] C. Thierry, V.G. Belille, F. Chausse, and J.")
![[17] P. Viola and M.](/docs-images/93/113664333/images/11-3.jpg "Jones, Rapid objec deecion using a boosed cascade of simple feaures, Proc.")
11 CVIM 4, pp.69 84, [14] D-II vol.j88-d-ii, no.8, pp , Aug [15] C. Thierry, V.G. Belille, F. Chausse, and J. Thierry, Real-ime racking wih classifiers, Inernaional Workshop on Dynamical Vision in Conjuncion wih ECCV, [16] J. Vermaak, A. Douce, and P. Perez, Mainaining muli-modaliy hrough mixure racking, Proc. IEEE Inernaional Conference on Compuer Vision, vol.2, pp , [17] P. Viola and M. Jones, Rapid objec deecion using a boosed cascade of simple feaures, Proc. IEEE Inernaional Conference on Compuer Vision and Paern Recogniion, vol.1, pp , [18] Y. Wang, J. Wu, and A. Kassim, Paricle filer for visual racking using muliple cameras, Proc. IAPR Conference on Machine Vision Applicaions, pp , [19] C. Yang, R. Duraiswami, and L. Davis, Fas muliple objec racking via a hierarchical paricle filer, Proc. IEEE Inernaional Conference on Compuer Vision and Paern Recogniion, vol.1, pp , LSI Ph.D in Roboics ACM IEEE ATR
IPSJ SIG Technical Report Vol.2010-CVIM-172 No /5/ Object Tracking Based on Generative Appearance Model 1. ( 1 ) ( 2 ) ( 3 ) 1 3) T
1 2 2 3 1 Objec Tracking Based on Generaive Appearance Model 1. ( 1 ) ( 2 ) ( 3 ) 1 3) Tasuya YONEKAWA, 1 Kazuhiko KAWAMOTO, 2 Asushi IMIYA 2 and Akihiro SUGIMOTO 3 We propose a mehod for racking objecs
KinecV2 2.2 Kinec Kinec [8] Kinec Kinec [9] KinecV1 3D [10] Kisikidis [11] Kinec Kinec Kinec 3 KinecV2 PC 1 KinecV2 Kinec PC Kinec KinecV2 PC KinecV2
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Kinec Developmen of Moion Capure Sysem using Muliple Kinecs 1 1 1 Miyaake Jumpei 1 Ohubo Masakazu 1 Yoshida Kaori 1 1 1 Graduae School of Life Science and Sysems Enginnering, Kyushu Insiue of echnology
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Fas Single-Image Defogging 谭志明 白向晖 王炳融 東明浩 あらまし CPU GPU720 48050 fps Absrac Bad weaher condiions such as fog, haze, and dus ofen reduce he performance of oudoor cameras. In order o improve he visibiliy
2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server
![2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server](/thumbs/91/107223763.jpg "2007/8 Vol. J90 D No. 8 Stauffer [7] 2 2 I 1 I 2 2 (I 1(x),I 2(x)) 2 [13] I 2 = CI 1 (C >0) (I 1,I 2) (I 1,I 2) Field Monitoring Server")
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xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL
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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
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2.2 6).,.,.,. Yang, 7).,,.,,. 2.3 SIFT SIFT (Scale-Invariant Feature Transform) 8).,. SIFT,,. SIFT, Mean-Shift 9)., SIFT,., SIFT,. 3.,.,,,,,.,,,., 1,
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5 インチ PDP カメラ (a) (b) 1 Fig. 1 Information display. (a) f=25mm (b) f=16mm 2 UXGA Fig. 2 Examples of captured image. [3] [4] 1 [5] [7] 1 3pixel 5 1 7pi
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THE INSTITUTE OF ELECTRONICS INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. 619 289 3 5 66 851 E-mail: {j-satakeakihiro-k}@nict.go.jp {hirayamakawashimatm}@i.kyoto-u.ac.jp UXGA 3fps
(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α,
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(3.6 ) (4.6 ) 2. [3], [6], [12] [7] [2], [5], [11] [14] [9] [8] [10] (1) Voodoo 3 : 3 Voodoo[1] 3 ( 3D ) (2) : Voodoo 3D (3) : 3D (Welc
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