局所特徴認識アルゴリズムによる車両の認識 - 池内研究室 - 東京大学

THE INSTITUTE OF ELECTRONICS,INFORMATION AND COMMUNICATION ENGINEERSTECHNICAL REPORT OF IEICE. y y yy yy 153{8505 4{6{1yy 676-8686 2{1{1E-mail: yfyoshida,kagesawa,kig@cvl.iis.u-tokyo.ac.jp, yytomonaka@wt.trdc.mhi.co.jp binary features binary features 95%ITSBinary features Vehicle Recognition with Local-Feature Based AlgorithmTatsuya YOSHIDA y , Masataka KAGESAWA y ,Tetsuya TOMONAKA yy , and Katsushi IKEUCHI yy Institute of Industrial Science, University ofTokyoKomaba 4{6{1, Meguro-ku, Tokyo, 153{8505 Japanyy Takasago Research &Development Center, Mitsubishi Heavy Industries, Ltd., Shinhama 2{1{1,Arai-cho, Takasago, Hyogo, 676-8686 JapanE-mail: yfyoshida,kagesawa,kig@cvl.iis.u-tokyo.ac.jp, yytomonaka@wt.trdc.mhi.co.jpAbstract In this paper, we describe a robust method for recognizing vehicles. Our system is based on local-featurecongration method, which is a generalization of eigen-window method. This method has following advances. It candetect even if part of vehicle is occluded, and it is robust against ilumination changes. We have developed our systemwith infrared images, but in this paper, we apply our system on images of super wide-angle nomal light images. Inoutdoor experiments, our system detects over 95% of vehicles.Key wordsITS, Vehicle recognition, Image processing, Eigen-window, Binary features method|1|

1. ITS ETC [1]2. [4] Binary Features [5] [3] Binary Features 2 2. 1 3 2 N nm n 2m = N M z 1 ;z 2 ;:::;z M c Z Z =[z 1 0 c; z 2 0 c;:::;z M 0 c] (1)1 Q Q = ZZ T (2) Q i e i (i =1;:::;N) i e i = Qe i ( 1> = 2> = ::: N> = 0) (3) th k ki=1W = iP >Ni=1 = th(k N) (4)i E =[e 1 ; e 2 ;:::;e k ] z i N i k3 2 2. 2 XG = ( @I @I)(@ @ )T (5)2Rmin( 1 ; 2 ) > (6) =(x; y) T 2RI 1 ; 2 G S l;m = k l 0 m k (7) l ; m z l ;z m |2|

Fig. 1 1 Eigen window technique. (7) T sim z l ;z m T i w i w i (T i ;z i ; x i ;y i )z i (x i ;y i ) T i J w w w(J; z; x; y)z (x; y) J V M 2 R 2 R M R2R XY w w k z z k w k V (T k ;x0 x k ;y0 y k ) V (T; x; y) r J (x; y) T r J th th 2. 3 Binary Features Binary Features 2 2 binary features2 r i (x; y) =min0d< dx< d= =0d< dy < d= =fD H [w(K; z; x; y);w(K; z d ; x + d x ;y+ d y )](d x ;d y ) j=(0; 0) (8)w(K; z; x; y): K (x; y)z D H (r; s) r; s Lloyd 1 0 2 2 3. Binary Features |3|

Models 9 Features 60Codes(m) 20 Window size(n 2 n) 7 2 7Threshold(T ) 65 Iteration(Lloyd) 10 1 Table 1 Parameters of experiments 2 Fig. 2 Flow-chart of the vehicle recognitionsystem. 2 [1]3. 1 2 (8) n 2 n Lloyd m 3. 2 2 I s (x; y) I s (x; y) n 2 n m 9 2 9 2 1 T 4. 3. 4. 1 4. 1. 1 2 1 3 9 9 9 2 2 3 30 60 |4|

3 Fig. 3 Model images of a white vehicle. 6 ::Fig. 6 Examples of experiments. Left imagesare input images, right imagesare recognition results. 4 Fig. 4 Extracted features.Success RatioWhite vehicle 135/137 98.5 %Black vehicle 129/227 56.8 %False alarms 13/259 5.0 % 2 Table 2 Results of experiments 5Fig. 5Codes made from features. 4 540 20 5 Lloyd 10 65 4. 1. 2 259 6 137 227 2 2 4. 2 4. 2. 1 7 2 2 1 0 2 I i (x; y)2 I b (x; y) I s (x; y)|5|

7 Fig. 7 Background subtraction.Success RatioWhite vehicle 141/143 98.6 %Black vehicle 226/234 96.6 %False alarms 18/262 6.9 % 3 Table 3 Results of experiments usingbackground subtraction.I s (x; y)= 1 (Ii (x; y)=1;I b (x; y)=0 )0 ()(9) I i (x; y) I s (x; y) 4. 2. 2 65 45 262 8 143 234 3 5. 95%DSRC 8 ::Fig. 8 Examples of experiments usingbackground subtraction. Left imagesare input images, right imagesare recognition results. [2] [1] M. Kagesawa, S.Ueno, K. Ikeuchi and H. Kashiwagi,Vehicle Recognition in Infra-red Images UsingParallel Vision Board, 6th World Congresson Intelligent Transport Systems '99, Toronto,U.S.A., Nov. 1999.[2] M. Kagesawa, A. Nakamura, K. Ikeuchi and H.Saito,\Vehicle Type Classication in Infra-red ImageUsing Parallel Vision Board," 7th WorldCongress on Intelligent Transport Systems 2000,Torino, Italy, Nov. 2000.[3] H, Murese and S. K. Nayar, \Visual Learning andRecognition of 3-D Object from Appearance," InternationalJournal of Computer Vision, vol.14,no.1, pp.5{24, Jan. 1995.[4] K. Ohba and K. Ikeuchi, \Detectabirty, Uniqueness,and Reliability of Eigen-Windows for StableVerications of Patrtially Ocluded Objects,"IEEE Pattern Recognition and Machine Intelligence,vol.19, no.9, pp.1043{1048, Sept. 1997.[5] J. Krumm, \Object Recognition with VectorQuantized Binary Features," Proc. of ComputerVision and Pattern Recognition '97, pp.179{185,San Juan, Puerto Rico, June 1997.|6|