i 1 1. [1] [2] [3] [13] Ruhr-Universitt Bochum Real-time Computer Vision The German Traffic Sign Recognition Benchmark(GTSRB)
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1 24
2 i 1 1. [1] [2] [3] [13] Ruhr-Universitt Bochum Real-time Computer Vision The German Traffic Sign Recognition Benchmark(GTSRB)
3 ii SVM 96.69%
4 iii i
5 iv A 33 A A A
6 [1] ABS(Antilock Brake system) VICS
7
8 [2]
9 [3] SIFT SIFT(Scale Invariant Feature Transform)[5] [6] SIFT SIFT Paulo Correia[7] [8] Curvature Scale Space CSS: [9] CSS CSS [10]
![Transform)[5] [6] SIFT SIFT 2.](/docs-images/46/20522871/images/page_9.jpg "2.3 Paulo Correia[7] [8] Curvature")
10 Fleyeh[11] Hue Saturation Value 2.4 [12]
![Fleyeh[11]](/docs-images/46/20522871/images/page_10.jpg "Hue")
11 R G B 3 [13]
![G B 3 [13] 2](/docs-images/46/20522871/images/page_11.jpg "3.1 3.2 3.3 3.")
12 :
13 Roberts Roberts u(45 ) v(135 ) u v 3.2 p (x, y) u v G u (p) G v (p) G (p) θ (p) G u (p) G v (p) G (p) θ (p) G (p) = G u (p) 2 + G v (p) 2 (3.1) θ (p) = tan 1 G v (p) G u (p) (3.2) (a) (b) 3.2: Roberts 3.3:
14 : 3.3 [15] HOG
![[15] 3.](/docs-images/46/20522871/images/page_14.jpg "5 3.4 HOG")
![3.4 10 [a] [b] [c] 3.5: [d] [13, 16] Roberts 1. I = I(p) p = (p x, p y ) 3 3 2.](/docs-images/40/20522871/images/15-0.png "Roberts p G(p) θ(p) G u (p), G v (p) p u v G (p) = G u (p) 2 + G v (p) 2 (3.")
15 [a] [b] [c] 3.5: [d] [13, 16] Roberts 1. I = I(p) p = (p x, p y ) Roberts p G(p) θ(p) G u (p), G v (p) p u v G (p) = G u (p) 2 + G v (p) 2 (3.3) θ (p) = tan 1 G v (p) G u (p) (3.4)
16 n x n y n 0 L n x n y L n x n y L 4. (2i, 2j) (i = 0, 1,, n 2 1, j = 0, 1,, m 2 1) 5 5 n x /2 n y /2 L [ ] L/2 L/2 [1 2 1] L/4 n x n y L 32 0 n 2 R G B z x = z z (3.5) y = x u (3.6) x u [17] u = 0.4
![/2 L [1 4 6 4 1] L/2 L/2 [1 2 1] L/4 n x n y 13 13 L 32 0 n 2](/docs-images/46/20522871/images/page_16.jpg "R G B 3 8 8 7 3 1176 5. z x = z z (3.5) 6. 3.6 3.6 y = x u (3.")
17 : Principal Component Analysis PCA PCA y 1 y n PCA n
18 (a)pca (b)pca 3.7: PCA j j = 1 2 m x = [ x 1 x 2 x n ] (3.7) M Σ t M = 1 m x j (3.8) m Σ t = j=1 m (x j M)(x j M) T (3.9) j=1 Λ Φ Σ t Φ = ΦΛ (3.10) d d (d n) x y y i = Φ T i x (i = 1 2 d) (3.11) y = [ ] T y 1 y 2 y i y d (3.12)
19 SVM(Support Vector Machine)[18] SVM 2 SVM 2 LIBSVM Ver.3.11[19] SVM, 2 cost RBF Radial Basis Function RBF ( K (x 1, x 2 ) = exp γ x 1 x 2 2), γ > 0 (3.13) 2 cost γ LIBSVM cost γ LIBSVM 2 x P (i x) SVM Multiclass-SVM
![3.11[19] SVM, 2 cost RBF Radial Basis Function RBF ( K](/docs-images/46/20522871/images/page_19.jpg "(x 1, x 2 ) = exp γ x 1 x 2 2), γ > 0 (3.")
20 , Ruhr-Universitt Bochum Realtime Computer Vision The German Traffic Sign Recognition Benchmark(GTSRB) [20] IJCNN 2011 competition[21] 4.1. GTSRB % GTSRB
![German Traffic Sign Recognition Benchmark(GTSRB) [20]](/docs-images/46/20522871/images/page_20.jpg "IJCNN 2011 competition[21] 4.1. GTSRB 43 4.1 30 4.2 4.")
21 4.1 標識画像データセット 16 図 4.1: GTSRB データベースの全 43 クラスの画像例 図 4.2: １トラックにおける標識画像例 三重大学大学院 工学研究科
22 : [a] [b] [c] 4.4: [d] [3] 4.5:
23 [a] [b] 4.6:
24 SVM 26,640 12,569 39,209 SVM (%) = SVM Gray Gray zero RGB RGB zero 2 0 RGB RGB : RGB 95.08% gray 95.44% gray zero 96.56% RGB zero 96.69% RGB IJCNN 2011 competition 4 [20] 4.2 Human Performance HOG1 HOG2 HOG3 HOG
25 IDSIA Committe of CNN+HOG % sermanet Multi-Scale CNNs 98.97% INI-RTCV Human Performance 98.81% VISICS IKSVM+PHOG+HOG % VISICS SRC+LDAs I/HOG1/HOG % noob HOG+LDA+VQ 96.87% INI-RTCV LDA+HOG % INI-RTCV LDA+HOG % INI-RTCV LDA+HOG % 4.2: Name Dimension Cell Block Stride Bins Semicircle HOG true HOG false HOG true 4.3: HOG
26 RGB Gray RGB zero Gray zero RGB RGB RGB (129,33,20) 2 (255,0,0) 4.16 Imagemagick convert Point-Spread Function(PSF, ) 4.16
27 :
28 : Gray
29 : Gray zero
30 : RGB
31 : RGB zero
32 : Gray
33 : Gray zero
34 : RGB
35 : RGB zero
36 :
37 SVM PCA 0 GTSRB 12, %
38 33 A /net/xserve0/users/hiroki/research/main main --65resize_Training_data/ # resize_Test_data/ # 65 --feature_extraction/ --pca_svm117600train # --pca_svm117600test # --pca_svm sh # --pca/
39 A.1 34 # --libsvm-3.11/ --svm-train # --svm-predict # A.1 c++ sh magickcompile [Input File] c gcc -o [Output File] [Input File] -lm A.2 feature extraction/ sh pca svm sh pca/ sh data preparation svm.sh SVM libsvm-3.11/./svm-train -g [gamma] -c [cost] [Input LearnData] [Output ModelData] gamma: cost: Input LearnData:
40 A.3 35 Output ModelData:./svm-predict [Input TestData] [Input ModelData] [Output File] Input TestData: Input ModelData:./svm-train Output File: A.3 24
41 36
42 37 [1] [2],,..,Vol.104, No.740, pp , March [3],,., Vol.99, No.609, pp.17 22,February [4],,,,.. D-II Vol.J82-D-II No.11 [5] David G. Lowe. Distinctive image features from scale-invariant keypoints. Journal of Computer Vision, 60, 2, pp ,2004. [6],. SIFT. 13 SSII07, LD2-06, June [7] C.F.Paulo and P.L.Correia. Traffic Sign Recognition Based on Pictogram Contours. Image Analysis for Multimedia Interactive Services, 2008, WIAMIS 08, Ninth International Workshop on, Klagenfurt, pp.67-70, May [8] M.Kass, A.Witkin, and D.Terzopoulos. Snakes: Active contour models. Int.J.Comput. Vis., vol.1,pp ,1988. [9] F.Mokhtarian, M.Bober. Curvature Scale Space Representation: Theory, Applications & MPEG-7 Standardization. Kluwer Academic Publishers, Dordrecht, [10] lvaro Enriquez de Luna, Carlos Miravet. A decision support system for ship identification based on the curvature scale space representation. SPIE Proceedings of SPIE Volume 5988, 59880K (Oct. 21, 2005). [11] H.Fleyeh, Traffic Sign Recognition by Fuzzy Sets. Intelligent Vehicles Symposium, 2008 IEEE, Eindhoven, pp , June [12] H.Ishida, T.Takahashi, I.Ide, Y.Mekada, and H.Murase. Identification of degraded traf-
43 38 fic sign symbols by a generative learning method. Pattern Recognition, 2006, ICPR 2006, 18th International Conference on, Hong Kong, pp , August [13],,,.. D-II, Vol.J77-D-II, No.10, pp , [14], pp , [15], pp , [16] Tetsushi Wakabayashi, Shinji Tsuruoka, Fumitaka Kimura and Yasuji Miyake. Increasing the feature size in handwritten numeral recognition to improve accuracy Systems and Computers in Japan, Volume 26 Issue 8, pp.35-44, [17],,,. (D-II) vol.j76-d-ii no.12 pp Dec.1993 [18] Vapnik, V. The Nature of Statistical Learning Theory Springer-Verlag, New York, [19] Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2:27:1 27:27, Software available at cjlin/libsvm [20] J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition in International Joint Conference on Neural Networks, 2011,accepted. [21] ruhr-universität bochum institut für neuroinformatik. Results for IJCNN 2011 competition German Traffic Sign Recognition Benchmark.
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Query-by-Dancing: WISS 2018. Query-by-Dancing Query-by-Dancing 1 OpenPose [1] Copyright is held by the author(s). DJ DJ DJ WISS 2018 [2 4] [5,6] Query-by-Dancing Query-by- Dancing Cao [1] OpenPose 2 Ghias
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