[12] [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12]
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1 Walking Person Recognition by Matching Video Fragments Masashi Nishiyama, Mayumi Yuasa, Tomokazu Wakasugi, Tomoyuki Shibata, Osamu Yamaguchi ( ), Corporate Research and Development Center, TOSHIBA Corporation [email protected] Abstract [1] [2] FacePass[3] 1(a) FacePassenger[4] 1(a) 1(b) 1 2 3
![2 4 2 3 4 3 [12] 2 3 4 5 1 1 [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1](/docs-images/39/20193688/images/2-0.png "[10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1")
2 [12] [5, 6, 7] [5, 6] [7] 1 [8] 1 1 [9] 1 [10, 11] [10] [11] 1 [13, 14] [13] [14] [13, 14] [10, 11, 13, 14] 1 [12]
![[10, 11] [10] [11] 1 [13, 14] [13]](/docs-images/49/20193688/images/page_2.jpg "[14] [13, 14] [10, 11, 13, 14] 1")
3 図6 カメラ内における断片的な動画像の生成 的な追跡処理を用いる 3 次元的な追跡を精度よく行う 図7 相互部分空間法による断片的な動画像同士 ために厳密なカメラキャリブレーションが要求される の比較 図5 段階的な対応付けの流れ 手法では 複数の歩行者の顔の様々な見え方を登録す るために 検出と追跡のタスクをそれぞれのカメラに 動的に割り当て顔画像の集合を生成する 各カメラか ら得られる人物毎の顔画像を対応付けるために 3 次元 また 運用中に何らかの原因でカメラの位置がずれる と追跡処理が破綻し識別性能が低下する ベルを判定する 同じラベルをもつ x を断片的な動画 3 段階的な対応付け 像 X に加える 一定の時間 T 1 以上新たな顔画像が追 複数カメラを用いて複数の歩行者を 動画像を用い た識別手法で個人識別するために カメラキャリブレー ション行うことなく顔画像を段階的に対応付け 人物 毎の動画像を生成する方法について述べる 加されなかった断片的な動画像 X は通過した人物と判 定し カメラ間の断片的な動画像の対応付けへ進む X のラベルを関数 M2 で判定し 同じラベルをもつ断片 的な動画像 X, X 0 を統合する 一定の時間 T 2 を経過し た断片的な動画像は対応付けが終了したと判断し 統 3.1 段階的な対応付けの枠組み 合された動画像 X とする この X を用いて個人識別を 最初に各カメラにおいて顔画像を対応付けし断片的 な動画像を生成する 断片的な動画像を式 (1) で定義 する 行う 3.2 断片的な動画像を生成するためのラベル付け 各カメラで獲得された顔画像 x は 関数 M1 により Xl {xi M1 (xi ) = l, i = 1,..., N } (1) 図 6 のように 同じカメラにおいて蓄積された断片的 な動画像と対応付けられる 対応付ける際には 断片 ここで x は 1 枚の顔画像 M1 は顔画像に対してラベ ルを返す関数 l は断片的な動画像に付けられたラベル 的な動画像に属する最新の顔画像 x X と x との間で 式 (3) の類似度 S を算出する N は獲得された顔画像の枚数を表す 関数 M1 につい S= ては 3.2 節で述べる 次に カメラ間で断片的な動画像 を対応付けし 個人識別で用いる統合された動画像 X Ssimple 1 + α(t t ) (3) ここで Ssimple は x, x 間の単純類似度 α は定数 t, t は を生成する X は式 (2) で定義される x, x が獲得された時間を表す 単純類似度は Ssimple = Xk {Xj M2 (Xj ) = k, j = 1,..., M } (2) ここで M2 は断片的な動画像に対してラベルを返す関 数 k は統合された動画像に付けられたラベル M は 獲得された断片的な動画像の個数を表す 関数 M2 に ついては 3.3 節で述べる 図 5 に 三台のカメラの下 で 二人の人物が歩行したときに段階的に対応付けさ れる流れを示す 実システム上では 顔画像は時間の経過と共に順に 獲得される 各カメラにおいて断片的な動画像を生成 するために 顔画像 x が獲得される毎に関数 M1 でラ cos2 θ で定義される θ は 顔画像をラスタースキャン することで変換されたベクトル同士のなす角度を表す 関数 M1 は 閾値 S1 を越え最も高い類似度が算出さ れた断片的な動画像のラベルを返す また 算出され た全ての類似度が S1 未満の場合 新たな人物が表れた と判定し 新たなラベルを返す 対応付ける断片的な 動画像が 1 個も蓄積されていない場合も新たなラベル を返す
![8 3.3 M 2 S 7 (OMSM Orthogonal Mutual Subspace Method)[15] OMSM OMSM M 2 S2 S2 3.4 OMSM X [16] O P, Q P Q S θ (4) S = cos 2 θ (4) θ = 0 cos 2 θ R Ra = λa (5) R = (r mn ) (m, n = 1.](/docs-images/39/20193688/images/4-0.png ".. D P ) (6) D Q r mn = (ψ m, φ l )(φ l, ψ n ) (7) l=1 ψ m, φ l P,Q m, l (ψ m, φ l ) ψ m φ l D P, D Q P, Q D P D Q 4 4.1 9 10 (a) (b) (c) (d) (e) (f) x 8 (i) (ii)3 [17] (iii) [18] 4.")
4 8 3.3 M 2 S 7 (OMSM Orthogonal Mutual Subspace Method)[15] OMSM OMSM M 2 S2 S2 3.4 OMSM X [16] O P, Q P Q S θ (4) S = cos 2 θ (4) θ = 0 cos 2 θ R Ra = λa (5) R = (r mn ) (m, n = 1... D P ) (6) D Q r mn = (ψ m, φ l )(φ l, ψ n ) (7) l=1 ψ m, φ l P,Q m, l (ψ m, φ l ) ψ m φ l D P, D Q P, Q D P D Q (a) (b) (c) (d) (e) (f) x 8 (i) (ii)3 [17] (iii) [18] 4.2 x Joint Haar-like AdaBoost [19] [20] [21] 4.3 [20]
5 (i) without occlusion (ii) with occlusion (a) (b) 10(f) [20] 2 η (8) 13 η = η + β(p 1 P 2 ) (8) η P 1 P β 2 (c) 2 (d) (e) 4.4 [20] (i),(ii) 3 (i) 3 (ii) pixels [17] [18] 1024 (i) 76 (ii) 59 S1 (i) A 19 B 5 C 4 (ii) A 7 B 8 C 11 (ii) 5.2
![4 [20] 5 5.1 1 1 11(i),(ii) 3 (i) 3 (ii) 1024 768 pixels 7.](/docs-images/49/20193688/images/page_5.jpg "5 1 7 14 12 64 64 [17] [18] 1024 (i) 76 (ii) 59 S1 (i) A 19 B 5 C 4 (ii)")
6 2 1 Camera CMR(%) EER(%) C C C All (i) (ii) (%) Camera (i) (ii) C C C All (C1, C2, C3) pixels (i) 5.1 All C1, C2, C (ii) C2, C3 C (CMR:Correct Match Rate) 2. (EER:Equal Error Rate) FAR( ) FRR( ) FAR F AR = (9) FRR F RR = (10) (1 ) C1, C2, C3 All C2, C3 C1 1(ii) 7 All
7 98 CMR(%) (i) without matching fragmented sequences (ii) with matching fragmented sequences (ii) ideal Number of individuals M False Matching Rate(%) Number of individuals M 17 EER(%) (i) without matching fragmented sequences (ii) with matching fragmented sequences (ii) ideal Number of individuals M C1, C2, C3 ( ) M M M 1 10 M 2 2 S2 0 M 16 2 All 349 M CMR 17 EER 18 (i) 2 C1, C2, C3 (ii) 18 (iii) 2 All 16 CMR EER EER (i) (ii) 7 (iii) (ii) 10 (i) CMR EER 6 349
8 5 89.9% 94.2% 8.3% 4.2% [1],, D-II Vol. J80-D-II, No. 8, pp , 1997 [2],,,, D-II Vol. J88-D-II, No. 8, pp , [3],,,,,, FacePass, Vol. 56, No.7, pp , 2002 [4],,,,,,, FacePassenger, FIT2005 I-010 pp.27-28, [5],,,, M. Jones, J. Thornton,, 10, pp , [6] Z. Yang, H. AI, B. Wu, S. Lao, and L. Cai, Face Pose Estimation and its Application in Video Shot Selection, International Conference on Pattern Recognition 2004, pp , [7] R. Chellappa, V. Kruger, and S. Zhou, Probabilistic Recognition of Human Faces from Video, The IEEE International Conference on Image Processing, Vol. I, pp , [8] K. S. Huang, and M. M. Trivedi, Streaming Face Recognition using Multicamera Video Arrays, International Conference on Pattern Recognition 2002, pp , [9],,,,, :, Vol. 43, No. SIG 4(CVIM 4), pp , [10],,,,,, D-II, Vol.J84-D-II, No.8, pp , [11],,,, 8, pp , [12],,,,, D-II, Vol.J84- D-II, No.3, pp , [13] J. G. Wang, R. Venkateswarlu, and E. T. Lim, Face tracking and recognition from stereo sequence, 4th International Conference on Audio- and Video-based Biometric Person Authentication, pp , [14],,,,,,, PRMU , pp , [15],,,, 2005-CVIM-151 (3), pp , [16] E. Oja, Subspace Methods of Pattern Recognition, Research Studies Press, England, 1983 [17] T. Kozakaya, and O. Yamaguchi, Face Recognition by Projection-based 3D Normalization and Shading Subspace Orthogonalization, 7th International Conference Automatic Face and Gesture Recognition, [18] M. Nishiyama, and O. Yamaguchi, Face Recognition Using the Classified Appearance-based Quotient Image, 7th International Conference Automatic Face and Gesture Recognition, [19] T. Mita, T. Kaneko, and O. Hori, Joint Haar-like Features for Face Detection, Tenth IEEE International Conference on Computer Vision 2005, pp , [20],,, (D-II), Vol. J80-D-II, No. 8, pp , Aug [21],,,, 6 (SI2005), pp , 2005.
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3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)
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Web, Web k-means 62% Associating Faces and Names in Web Photo News Akio Kitahara and Keiji Yanai We propose a system which extracts faces and person names from news articles with photographs on the Web
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色の類似性に基づいた形状特徴量CS-HOGの提案
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28 Horizontal angle correction using straight line detection in an equirectangular image
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1,a) 1,b) Obstacle Detection from Monocular On-Vehicle Camera in units of Delaunay Triangles Abstract: An algorithm to detect obstacles by using a monocular on-vehicle video camera is developed. Since
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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 +
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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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19 Development of moving image delivery system for elementary school 1080337 2008 3 10 Web Basic Web SAS-2 Web SAS-2 i Abstract Development of moving image delivery system for elementary school Ayuko INOUE
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第 1 回バイオメトリクス研究会 ( 早稲田大学 ) THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS Proceedings of Biometrics Workshop,169
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS Proceedings of Biometrics Workshop,169-8555 3-4-1,169-8555 3-4-1 E-mail: s [email protected], [email protected] Wolf
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258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.
光学
Range Image Sensors Using Active Stereo Methods Kazunori UMEDA and Kenji TERABAYASHI Active stereo methods, which include the traditional light-section method and the talked-about Kinect sensor, are typical
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