1 1 1 Detection and Recognition of Traffic Signal Using Machine Learning Akihiro Nakano, 1 Hiroshi Koyasu 1 and Hitoshi Maekawa 1 To improve road safety by assisting the driver, traffic signal recognition by on-board camera is one of required technologies. We have proposed to detect traffic signals and to keep tractracking them by template matching and Kalman filtering. However, its stability of detection is not robust enough to illumination variation or changes in appearance of the signals to detect. To adapt these kind of changes in circumstances, we present recognition method of traffic signal using machine learning approach in which we employ AdaBoost and support vector machine(svm) technique. We conduct experiments to evaluate its performance for several hundreds sample images. 1. ITS(Intelligent Transport System ) 11) 3) 6) 7) 8) () 5) 1 Graduate School of Science and Engineering,Saitama University 1
1) 2 2. 2.1 1 2 2.2 Haar-like 2 Haar-like Haar-like 2 Haar-like 2 3 T (W, B) S(W ) S(B) T (W, B) = S(W ) S(B) (1) 3 Haar-like 1 2 Fig. 1 Flow of proposed method which consists of learning phase and recognition phase. 3 Haar-like 2 Haar-like Haar-like Haar-like AdaBoost 2.3 AdaBoost Haar-like AdaBoost AdaBoost AdaBoost ( 1 ) N (x 1, y 1 ),..., (x i, y i ),..., (x N, y N ) x i Haar-like y i x i x i y i = 1 x i y i = 0 2
2 Haar-like Fig. 2 Haar-like differential operators in use. ( 4 ) F (x) F (x) = sign( M m=1 α my m (x)) ε m α m 2.4 HOG 2 Histogram of Oriented Gradients(HOG) HOG HOG 5 5 (x, y) I(x, y) m θ m(x, y) = f x (x, y) 2 + f y (x, y) 2 (2) θ(x, y) = tan 1 f y (x, y) f x (x, y) { f x (x, y) = I(x + 1, y) I(x 1, y) f y (x, y) = I(x, y + 1) I(x, y 1) (3) (4) Fig. 3 3 Haar-like Harr-like operators to detect features of signal light. ( 2 ) w i w i = 1, (i = 1,..., N) N ( 3 ) M m = 1,..., M step1 f m (x) w i step2 ε m = α m = log{ 1 εm ε m } step3 w i w i = w i exp{α mf m(x)} M w if m (x) i=1 N i=1 w i m θ θ 0 360 0 180 20 9 3 3 1 1 1 9 1 9 1 9 9 = 81 i j H ij = [h 1,, h 9 ] k V k V k = [H ij, H i+1j, H i+2j, H ij+1,, H i+2j+2 ] (5) v ijl 3
x 1 x 2 K(x 1, x 2) v ijl = 4 5 5 9 Fig. 4 Cell division of signal image. h ijl Vk 2 +ɛ 2 (6) h ijl i j l ɛ = 1 2.5 SVM HOG HOG Support Vector Machine(SVM) SVM SVM 2, x1 x2 2 K(x 1, x 2) = exp( ) (7) 2σ 2 2.6 Hough 1 Hough 1) 2 2 Hough 5 2 (a) (d) 2 (g) (e) (b) (h) (f) (c) (i) 3. 3.1 1 3.2 6 6 a b Haar-like AdaBoost a b 4
(a) (b) 5 2 Fig. 5 Some results of signal-light extraction. Top row: imput images, middle row: binarized images, and bottom row: recognition results. Table 1 1 Specifications of camera used. (c) Canon ivis HF11 640 480[pixel] 30[fps] 8:00 16:00 c d HOG SVM c d 2 1) (d) 6 (a) (b) Haar-like AdaBoost (a) (b) (c) (d) HOG SVM (c) (d) Fig. 6 (a)-(b): Detection example of signal in machine learning for Haar-like operators and AdaBoost. (a) is success example. (b) is failure example. (c)-(d): Detection example of signal in machine learning for HOG operators and SVM. (c) is success example. (d) is failure example. 2 4 5
2 Table 2 Performance of red signal detection. 210 109 101 52% Haar-like+AdaBoost 210 151 59 72% HOG+SVM 210 133 77 63% 3 Table 3 Performance of yellow signal detection. 116 45 71 39% Haar-like+AdaBoost 116 70 46 60% HOG+SVM 116 54 62 46% 4 Table 4 Performance of blue signal detection. Hough 7 (a) Hough (b) Haar-like+AdaBoost (c) (a) (b) Hough 204 86 118 42% Haar-like+AdaBoost 204 155 49 75% HOG+SVM 204 131 73 64% 3 Haar-like+AdaBoost 60% HOG+SVM 46% Haar-like+AdaBoost HOG+SVM HOG+SVM Haar-like+AdaBoost HOG+SVM HOG 1 Haar-like+AdaBoost 3.3 Haar-like+AdaBoost HOG+SVM 1) 1) (a) (b) (c) 7 (a) (b) Haar-like +AdaBoost (c) (a) (b) Fig. 7 (a): Detection of signal light. (b): Detection of signal only machine learning result. (c): Result of detection by (a) and (b). 6
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