3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)

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1 (MIRU2012) {d (1) (2) (3) (4) 4 AdaBoost 1. Kanade [6] CLAFIC [12] EigenFace [10] [7]

2 3 2 2 (1) (2) (3) (4) 4 4 AdaBoost 2. [11] Onishi&Yoda [8] Iwashita&Stoica [5] 4 [3] 3. 3 (1) (2) (3)

3 720 図 4 人物領域抽出例 特徴量抽出 この節では人物識別に用いる特徴量について述べる 図 5 人物の立ち位置による の出力結果の違い 本手法では (1) 人物の身長 (2) 人物の面積 (3) 人物 の体型 (4) 深度ヒストグラムの 4 つを特徴量を扱って いる 注 2 本手法における身長特徴は からの距離であり 実際の 人物の身長ではないことに注意 注 3 本論文における人物の体型特徴は頭部や肩の領域の幅 図 6 正規化の例 y 人物の身長 人物の身長は人物間の区別において最も重要な特徴量 の一つになる 本手法では深度情報を利用することで人 物の身長に関する特徴量を求める カメラから最も近い 位置にある人物の頭部までの距離は人物ごとに異なる そこで それぞれの深度画像においてカメラから最も近 いピクセルまでの距離を抽出する このようにして人物 の頭部までの距離は得ることができるが 立ち位置によ る距離値の差という問題がある 図 5 はその問題の例を 表している. この図において 下の値はそれぞれ から得られた距離を示している この値から分かるよう に人物の立っている位置によって同一人物においても特 徴量に大きく差が出てしまう 注 2 この問題を解決するために人物の立ち位置により出力 値を正規化する 本手法では深度情報を利用することで 概ね正しく人物領域を推定することができる こうして 求められた人物領域の中心位置を基に正規化することで 同一人物内における誤差を減少させることができる 図 6 は正規化の例を表している. このようにして得られた 値を人物の身長特徴とする 人物の面積 人物の面積も人物識別において直感的で特有の特徴量 となる そこで本手法では人物領域における面積の値を 利用する この特徴量は 3. 1 節において抽出された人物 領域のピクセル数の総和を算出することにより求める 人物の体型 人物の身体の大きさ 注 3 は人物識別において最も効果 的な特徴量の一つになる 本手法では 体型特徴として x 図 7 人物の体型特徴の取得例 x 座標と y 座標における幅の大きさを使用している こ れらの値は人物領域画像の周辺分布から抽出する 人物 領域の x 座標と y 座標において幅が最大となる値を使用 する 図 7 は体型特徴の取得例を表している

4 AdaBoost [2] AdaBoost Adaboost 1 2 C4.5 [9] 4 C AdaBoost Weka leave-one-out ,,, 3. 2,, 4 Weka J48 5

5 1 [%] all , +. all [%] 1 (94.4%)., % A H A H E G % 5 6cm 2 3cm 52.5% 21.9% 40.6% [7] x y Gallagher&Chen [4] [13]

6 2 + A B C D E F G H A B C D E F G H (1) (2) (3) (4) 4 AdaBoost C4.5 (1) (4) % 6 [1] pp , [4] A. C. Gallagher and T. Chen. Using Context to Recognize People in Consumer Images, IPSJ Transactions on Computer Vision and Applications, Vol. 1, pp , [5] Y. Iwashita and A. Stoica. Gait Recognition using Shadow Analysis, Proc. of Symposium on Bioinspired, Learning, and Intelligent Systems for Security 2009, pp , [6] T. Kanade. Picture processing by computer complex and recognition of human face, Technical report, Kyoto University, Dept. of Information Science, [7] R. Nakatani, D. Kouno, K. Shimada and T. Endo. A Person Identification Method Using a Top-view Head Image from an Overhead Camera, Proc. of 2nd International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2011), SS4-1, [8] M. Onishi and I. Yoda. Visualization of Customer Flow in an Office Complex over a Long Period, Proceedings of International Conference on Pattern Recognition (ICPR), pp , [9] J. R. Quinlan. C4.5 Programs for Machine Learning, Morgan Kaufmann Publishers, [10] M. Turk and A. P. Pentland. Eigenfaces for recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp , [11],,, 129, No. 6, pp , [12] S. Watanabe and N. Pakvasa. Subspace method in pattern recognition, Proc. of 1st Int. J. Conf on Pattern Recognition, pp. 2-32, [13] Vol 23 No 2 pp [1] M. Farenzena, L. Bazzani, A. Perina, V. Murino and M. Cristani. Person re-identification by symmetrydriven accumulation of local features Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp , [2] Y. Freund and R. E. Schapier. Experiments with a new boosting algorithm, Proc of ICML pp , [3],,, 2008, Vol.J91-D, No. 5, 6

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