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1 Real AdaBoost HOG A Graduation Thesis of College of Engineering, Chubu University Efficient Reducing Method of HOG Features for Human Detection based on Real AdaBoost Chika Matsushima

2

3 ITS Graphics Processing Unit (GPU) [1] Large Scale Integration (LSI) Field Prgramble Gate Array (FPGA) [2] Edge of Orientation Histograms (EOH) [3] Histograms of oriented gradients (HOG) [4] Y.Linde Linde-Buzo-Gray (LBG) [6] Real AdaBoost HOG HOG HOG Real AdaBoost iii

4 3 1 2 Real AdaBoost HOG 3 iv

5 LBG [6] Real AdaBoost HOG HOG HOG HOG Real AdaBoost v

6 vi

7 LBG HOG HOG Real AdaBoost Real AdaBoost vii

8

9 ix

10

11 ( 1.1 ) 1. LBG LBG 1

12 LBG [6] 1.1: LBG Y.Linde [6] 1 LBG LBG ( 1.2 ) 2 1. (M= ) 1 (L ) ( ) k {x 1, x 2,, x k } x (1.1) x = C (x 1, x 2,, x k )=argmin x n 1 1 n i=0 d (x, x i ) (1.1) d (x, x i ) x x i argmin f (x) f (x) x x x 2.

13 1.2. M ɛ 2 ɛ 1/ ( ) ( ) 1.2: LBG M

14 1 1.3: N d x n = [x n1,x n2,x n3,,x nd ] T M S (1.2) (1.3) M = 1 N S = 1 N N x n (1.2) n=1 N (x n M)(x n M) T (1.3) n=1 2 S 1.3 Su j = λ j u j λ j u j d d (u 1 u 2 u d ) 4

15 : 5

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17 2 Real AdaBoost HOG Real AdaBoost HOG 2.1 HOG Dalal Histograms of oriented gradients(hog) [4] HOG 2.1 (5 5 ) HOG Step1 Step2 Step3 HOG HOG

18 2 Real AdaBoost HOG : HOG L(x y) m θ (2.1) (2.2) m(x, y) = (L(x +1,y) L(x 1,y)) 2 +(L(x, y +1) L(x, y 1)) 2 (2.1) θ(x, y) = tan 1 ((L(x, y +1) L(x, y 1))/(L(x +1,y) L(x 1,y))) (2.2) 8

19 2.1. HOG : i j (i, j) (8 ) F ij =[f 1 f 1 f 8 ] k (72 V k =[F ij F i+1j F i+2j F ij+1 F i+1j+1 F i+2j+1 F ij+2 F i+1j+2 F i+2j+2 ] L2-norm (2.3) Vf = f V ɛ 2 (2.3) = ( ) 1 HOG 23,040 (22.5 ) 9

20 2 Real AdaBoost HOG 2.2 HOG 1 HOG 2.3 HOG 2.3: HOG ( ) HOG Real AdaBoost 2.4(a) 8 1 (256 = 2 8 ) 2 1 W +,W 0 (2.10) h(x) 10

21 2.4. Real AdaBoost 2.4: A B C 2.4 Real AdaBoost Real AdaBoost Real AdaBoost 2.5 Real AdaBoost Real AdaBoost 2.3 Real- AdaBoost 2.4(b) 11

22 2 Real AdaBoost HOG 2.5: Real AdaBoost 1 h(x) (a) A B C 2.7 RealAdaBoost 12

23 2.4. Real AdaBoost 2.6: Real AdaBoost D D 1,i = 1 N (2.4) W + W W ± 1 D t W+ = D t (i) (2.5) W = i: J y i =+1 i: J y i = 1 D t (i) (2.6) 13

24 2 Real AdaBoost HOG t i j y i y {1, 1} i j D t (i) W ± W ± h j (x) (2.10) j = arg max 0 j bin ( h j(x) ) arg min 0 j bin ( W j + W j ) max ( h j(x) ) 2 0 j bin otherwise (2.7) j W +,W h j (x) (2.14) H(x) 14

25 2.4. Real AdaBoost r j = W j + W j (2.8) j j HOG 8 1 (2.8) 2.7: j j 0 j j = arg min 0 j <8 (r j r j ) (2.9) j j 15

26 2 Real AdaBoost HOG W + W h(x) x j W ± h(x) h(x) = 1 2 ln W + + ɛ W + ɛ (2.10) ɛ 0 ɛ = W ± Z Z =2 j W j +W j (2.11) Z Z t h t = arg min Z t,m (2.12) (2.13) D t+1 (i) =D t (i)exp[ y i h t (x i )] (2.13) 16

27 2.4. Real AdaBoost T H(x) = sign( h t (x)) (2.14) t=1 t h(x) 17

28

29 Positive 2,053 Negative 6,253 Positive HOG 1,000 1,234 (Miss Rate) (False Positive Rate) Detection Error Tradeoff(DET) 19

30 3 3.1: HOG % 30% HOG (VQ) 5.0% 1 ( )

31 : % 98.0% 3.1: 1 [%] [MB] HOG [4]( ) HOG(64 VQ)

32

33 HOG Real AdaBoost HOG 2.0% 23

34

35 25

36

37 [1] L.Zhang and R.Nevatia: Efficient Scan-Window Based Object Detection using GPGPU, Computer Vision and Pattern Recognition [2] N.Vinod, L.Pierre-Olivier, and C.James J.: An FPGA-Based People Detection System, EURASIP Journal on Applied Signal Processing, Vol.2005, pp , [3] K.Levi and Y.Weiss: Learning Object Detection from a Small Number of Examples: the Importance of Good Features, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2004, Vol.2, pp.53-60, [4] N.Dalal and B.Triggs: Histograms of Oriented Gradients for Human Detection, In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2005, Vol.1, pp , [5] B.M.Oliver, J.R.Pierce, and C.E.Shannon: The Philosophy of PCM, Proceedings of the Institute of Radio Engineers, vol.36, no.11, pp , [6] Y.Linde, A.Buzo, and R.M.Gray: An Algorithm for Vector Quantizer Design, IEEE Trans. on Communications, vol.28, no.1, pp.84-95,

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39 Real AdaBoost HOG ( )

IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing

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