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1 (MIRU202) AdrianStoica Jet Propulsion Laboratory 4800 Oak Grove Drive, Pasadena, CA 909, USA 2 nearest neighbor [] [2] Gait Energy Image(GEI) [2] [3] [4] [5] [6] [7] [5]

2 (a) (b) 2 2 (Bosch, IR Illuminator 850 nm, UFLED30-8BD) ( 7[m] 6[m]) 3 (PointGrey Research Inc.Grasshopper2 M/C) Hz (a) (b) IR light Walking direction Camera Invisible shadow for the subject 2 Captured image IR light 6m 7m Invisible shadow for the subject

3 ここで座標 (x, y) は対象人物の領域 O を基準とした座標 であり また I(x, y) は画像の輝度値を示す 式 を離散 化すると xp y q I(x, y) (2) と表される 式 2 より重心の位置は次の式で表される xg = µ0 µ0, yg = (3) と表される 式 2 と式 3 より重心周りのモーメントは次 の式で表される 図 3 構築した実験スタジオ (x xg )p (y yg )q I(x, y) (4) またモーメントは形状の特徴を示しており 0 次モー メントは対象物体領域の面積 2 次モーメントは対象の 重心周りの分散 3 次モーメントは対象物体の歪度を示 す 以下に本研究で用いる 22 個の 2 次元アフィンモー メント不変量 I = {I, I2,...., I22 } のうちの 6 個を示 す [9] [0] 図4 (µ20 µ02 µ2 ) µ400 I2 = 0 (µ230 µ203 6µ30 µ2 µ2 µ03 + 4µ30 µ32 + 4µ03 µ32 I = 撮影された画像の例 ついて述べる まず輝度値の差に基づく背景差分法により 撮影画像 から実際の人物領域 及び影領域を抽出する 図 5(a) に 実際の画像 図 5(b) に背景差分結果を示す 3µ22 µ22 ) I3 = 7 (µ20 (µ2 µ03 µ22 ) µ (µ30 µ03 µ2 µ2 ) +µ02 (µ30 µ2 µ22 )) I4 = (µ320 µ203 6µ220 µ µ2 µ03 6µ220 µ02 µ2 µ03 +9µ220 µ02 µ22 + 2µ20 µ2 µ2 µ03 +6µ20 µ µ02 µ30 µ03 8µ20 µ µ02 µ2 µ2 8µ3 µ30 µ03 6µ20 µ202 µ30 µ2 + 9µ20 µ202 µ22 +2µ2 µ02 µ30 µ2 6µ µ202 µ30 µ2 + µ302 µ230 ) I5 = 6 (µ40 µ04 4µ3 µ3 + 3µ222 ) I6 = 9 (µ40 µ04 µ22 + 2µ3 µ22 µ3 µ40 µ23 µ04 µ23 µ322 ) (5) 図 5 (a) データベースにおける歩行画像の例, (b) 抽出された 対象人物領域 3. 2 歩行周期の切り出し 次に抽出した影領域より 歩行特徴として 2 次元ア 前節で求めた 2 次元アフィンモーメント不変量の第 フィンモーメント不変量を抽出する アフィンモーメン 次モーメントである I に注目すると 図 6 に示すよう ト不変量はモーメントベースの記述子であり 一般的な に周期性があり また極大値は両脚支持期であることが アフィン変換に対して不変である 二次元平面上におけ わかる そこで 最初に極大値を示すフレームから 3 つ る (p + q) 次のモーメントは次の式で表される xp y q I(x, y)dxdy. 目に極大値を示すフレームまでを 一歩行周期と定義し て切り出す ()

4 6 I k-nearest neighbour (knn) 2 knn (4 ) (a) (b) [5] 2 [5] [] Leave-one-out cross validation 9, 2, 4, 8 4 (CCR) [%] The division number CCR[%] [5]

5 2 [5] [%] The division number CCR[%] [] 95 no., pp.67 74, 993. [0] J. Flusser, T. Suk, and B. Zitova, Moments and Moment Invariants in Pattern Recognition, Wiley & Sons Ltd., [],,, Adrian Stoica, vol., no. 430, PRMU20-98, pp. 7-75, (B) [] S. Lee, Y. Liu & R. Collins: Shape Variation-based Frieze Pattern for Robust Gait Recognition, Computer Vision and Pattern Recognition, pp. 8, [2] J. Han & B. Bhanu: Individual Recognition Using Gait Energy Image, IEEE Trans. PAMI, vol. 28, no. 2, pp , [3] J. Acquah, M. Nixon & J. Carter: Automatic gait recognition by symmetry analysis, Pattern Recognition Letters, 24, pp , [4] K. Sugiura, Y. Makihara & Y. Yagi: Gait Identification based on Multi-view Observations using Omnidirectional Camera, Asian Conf. on Computer Vision, Vol., pp , [5] Y. Iwashita & R. Kurazume: Person identification from human walking sequences using affine moment invariants, Proc. IEEE Int. Conf. Robotics and Automation, pp , [6] A. Stoica: Towards Recognition of Humans and their behaviors from Space and Airborne Platforms: Extracting the Information in the Dynamics of Human Shadows, Bio-inspired, Learning and Intelligent Systems for Security, [7] Y. Iwashita, A. Stoica & R. Kurazume: Person Identification using Shadow Analysis, British Machine Vision Conference, pp.35. 0, 200. [8] R. Green, Spherical Harmonic Lighting: The Gritty Details, SCEA Research and Development, [9] J. Flusser, and T. Suk, Pattern recognition by affine moment invariants, Pattern Recognition, vol.26,

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