IPSJ SIG Technical Report GPS LAN GPS LAN GPS LAN Location Identification by sphere image and hybrid sensing Takayuki Katahira, 1 Yoshio Iwai 1
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1 1 1 1 GPS LAN GPS LAN GPS LAN Location Identification by sphere image and hybrid sensing Takayuki Katahira, 1 Yoshio Iwai 1 and Hiroshi Ishiguro 1 Self-location is very informative for wearable systems. In this paper, we propose a method for identifying user s location from azimuth-invariant features extracted from sphere images, GPS data and wireless LAN data. User s location is independently recognized from the image feature, GPS data and wireless LAN data projected into a sub-space made from learning data. User s location is determined from the results. We show the effectiveness of our method by experimental results in real images, GPS data and wireless LAN data. 1. 1) 1 Graduate School of Engineering Science, Osaka University 2) 3) 4) 5) 6),7) GPS 8) LAN 9) 10) 11) LAN GPS 5) 12),13) 14) 5),12),13) 15) GPS GPS 16) GPS 1 c2010 Information Processing Society of Japan
2 LAN PC LAN PlaceEngine 17) LAN GPS LAN GPS LAN , (θ, φ) (θ, φ) f (θ, φ) (1) X(r) = 2π π 0 0 f (θ, φ)dθdφ = 3 2π π 0 0 θ,φ f (θ +Δ θ,φ+δ φ )dθdφ (1) Δ θ,δ φ Δ θ,δ φ (1) GPS LAN 3.1 GPS LAN GPS LAN 4 GPS LAN Dragonfly2(PointGrey) 185 YV2.2x1.4A-2(FUJINON) 2 c2010 Information Processing Society of Japan
![5 4 1024 768[pixels] GPS GPSMAP60CSx (GARMIN) LAN PC 5 GPS LAN k-means LAN LAN GPS 3.4 3.](/docs-images/98/138364964/images/3-0.jpg "2 GPS LAN GPS LAN 1970 1 1 0 0 0 3.2.1 R G B R G B Y l,m (3) l, m m l 3 c2010 Information Processing Society of Japan")
3 [pixels] GPS GPSMAP60CSx (GARMIN) LAN PC 5 GPS LAN k-means LAN LAN GPS GPS LAN GPS LAN R G B R G B Y l,m (3) l, m m l 3 c2010 Information Processing Society of Japan
4 Y l,m (θ, φ) = ( 1) (m+ m ) 2 K m l P m l (cos θ)e imφ (2) K m 2l + 1 (l m ) l = 4π (l + m ) P l m (t) = (1 t2 ) m 2 2 l [(l m)/2] k=0 ( 1) k (2l 2k)! k!(l k)!(l 2k m)! tl 2k m X l,m (3) k 2π π X l,m = image(θ, φ)y l,m (θ, φ)dθdφ 0 0 k k/2 image(2π j/k, 2πi/k)Y l,m (2π j/k, 2πi/k) i=0 j=0 { (l, m) l = 0, 2, 4, 6, m l } 16 R,G,B 48 (4) x img x img = (X R0,0 X G0,0 X B0,0 X R2,0 X G2,0 X B2,0 X R2,1 X G2,1 X B2,1 X R6,5 X G6,5 X B6,5 X R6,6 X G6,6 X B6,6 ) T (4) x img d img (x img, y img ) (5) x img,i x img i N d img (x img, y img ) = GPS N x img,i y img,i (5) i=0 GPS [m] d gps (x gps, y gps ) (6) x gps,lat, x gps,lon, x gps,ele, A d gps (x gps, y gps ) = x gps y gps = Δ 2 1 +Δ (x gps,ele y gps,ele ) 2 (6) Δα = y gps,lat x gps,lat Δ 1 = AΔα cos x gps,lon Δβ = y gps,lon x gps,lon Δ 2 = AΔβ (3) LAN LAN LAN x lan, y lan s lan (x lan, y lan ) (7) x lan,i x i n n x lan,i y lan,i s lan (x lan, y lan ) = i=0 3.3 x lan y lan k- means k-means C i 3.4 (7) (i = 1, 2,, n) GPS LAN C t t p(c t C t 1 ) C t 1 C t p(c t C t 1 ) k-nn S img (x img, y img ) (8) S img (x img, y img ) = p(c i C j ) exp{ d img (x img, y img )} (8) k k n i C i (i = 1, 2,, n) k #C i x img (9) C = arg max j=1 n #C i x img C (9) 4 c2010 Information Processing Society of Japan
5 3.4.2 GPS C i μ(c i ) (i = 1, 2,, n) 1-NN (10) S gps (x gps, y gps ) S gps (x gps, y gps ) = p(c i C j ) exp{ d gps (x gps y gps )} (10) n x gps (11) C = arg max i=1 n S gps(x gps,μ(c i )) x gps C (11) LAN (12) S lan (x lan, y lan ) S lan (x lan, y lan ) = p(c i C j ) s lan (x lan, y lan ) (12) LAN k-nn k k x lan (13) C = arg max i=1 n #C i x lan C (13) GPS LAN GPS LAN 4. GPS LAN 4.1 GPS LAN , FAR( False Acceptance Rate ) FRR( False Rejection Rate ) 1 GPS LAN 16 A a 17 B a 18 C a 19 D a 20 D b 21 E b 22 D c 23 D d GPS LAN 24 D e 25 D f 26 D g 27 D h 28 D i 29 D j 30 F k fold Cross Validation 5 c2010 Information Processing Society of Japan
6 μ = 0,σ= 2 N(μ, σ) k-means k-means GPS LAN i j i j 2 ( ) FAR( ) FRR( ) GPS LAN c2010 Information Processing Society of Japan
7 GPS LAN 11 6 LAN GPS 5. GPS LAN GPS LAN k-means k-nn GPS LAN 10 GPS 11 LAN 1) K. Tsukada and M. Yasumura, Activebelt: Belt-type wearable tactile display for directional navigation, Proceedings of UbiComp2004, Springer LNCS3205, pp , ) N. Kern, S. Antifakos, B. Schiele, and A. Schwaninger, A model for human interruptability: Experimental evaluation and automatic estimation from wearable sensors, In Proc.of the Eighth IEEE Intl.Symposium on Wearable Computers, Vol. 1, pp , ) M. Fukumoto and Y. Tonomura, Body coupled fingering: Wireless wearable keyboard, Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 97), pp , Apr ) Vol. PRMU pp ) 7 c2010 Information Processing Society of Japan
8 12 3 (FIT) Vol. 3 No. K-097 pp ) HyperOmni Vision D Vol. J79-D2 No. 5 pp ) PRMU pp ) H.S. Lee, K. Mase, T. Adachi, T. Oosawa, K. Nakano, M. Sengoku, H. Hidaka, N. Shinagawa, and T. Kobayashi, Pedestrian tracking using GPS, pedometer and magnetic compass, Transactions of IEICE B, Vol. J84-B, No. 12, pp , ) GPS Vol UBI-6 pp Nov ) Vol. 18 No. 3 pp ) Vol. 21 No. 6 pp ) K.T. Simasarian, T.J.Olson, and N. Nandhakumar, View-invariant regions and mobile robot self-localization, IEEE Trans. on Robotics and Automation, Vol. 12, No. 5, pp , ) R. Talluri and J.K. Aggarwal, Mobile robot self-localtion using model-image feature correspondence, IEEE Trans.on Robotics and Automation, Vol. 12, No. 1, pp , ) Y. Yagi, K. Imai, K. Tsuji, and M. Yachida, Iconic memory-based omnidirectional route panorama navigation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 1, pp , ).CVIM Vol No. 42 pp May ) GPS, B Vol. J84-B No. 12 pp Jan ) PlaceEngine: WiFi 2006 pp c2010 Information Processing Society of Japan
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