IPSJ SIG Technical Report Vol.2015-UBI-47 No.23 Vol.2015-ASD-2 No /7/ , HOG Parameter Estimation from Videos in Monocular Camera for Eva
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- あゆみ すみだ
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1 1 2 1, HOG Parameter Estimation from Videos in Monocular Camera for Evaluation System of the Bowing Action Abstract: Bowing is a symbol of greeting culture in Japan, and it is an important action for smooth communication. Learning a correct bowing action is essentials in the business scene. People can learn a correct bowing action by some methods, such as to use books and internet by oneself, and to attend manner lectures. However, the former method has a problem that we cannot evaluate the correctness of own bowing action without an instructor. The latter method has a problem that to attend manner lectures is not easy, cost, and takes time. In this paper, to solve these problems, developing a system which evaluates the user s bowing actions from videos taken by a monocular camera, we aim to create an environment that people can learn a bowing action by oneself easily. Towards implementation of this system, we defined posture, angle, and time as parameter for evaluation of the user s bowing action. We evaluated the accuracy of our proposed system which measures above-mentioned parameters from videos taken by a monocular camera. There are almost no systems which evaluate a user s bowing action. Keywords: Bowing action learning system Image processing Histogram of oriented gradients Optical flow 1. 1 Graduate School of Natural Science & Technology, Kanazawa University 2 Tokyo Healthcare University 3 1
![2. 2.1 [1] 1 (1) (2) (3) (4) 2.](/docs-images/103/157236296/images/2-0.jpg "2 [1] 3 2(a) (c) (a) (b) (c)")
2 [1] 1 (1) (2) (3) (4) 2.2 [1] 3 2(a) (c) (a) (b) (c) 15 ( ) (a) Fig. 1 Fig. 2 1 Process of the bowing action (b) Types of the bowing action. (c) ( 1 ) 1 ( 2 ) ( 3 ) 2
![距離情報を用いて回帰分析により姿勢推定を行っているも の [3] がある しかし これらの手法では動画撮影に複数 (b)hog 特徴量可視化画像 (a) 入力画像 図 4 HOG 特徴量の抽出 Fig. 4 HOG Extraction.](/docs-images/103/157236296/images/3-1.jpg "のカメラや特殊なカメラが必要となる 本研究の目的は個 人が自宅でお辞儀習得できる環境を構築することであり 一般的な家庭にある単眼カメラのみを用いるべきである そこで本研究では 大西らの研究 [4] を参考に 物体の形 3. 提案手法 状を表すことが可能な HOG 特徴量 (3.2 節) を用いて 人 3.")
3 情報処理学会研究報告 (a) 入力画像 (b) 背景画像 (c) 背景差分法 (a) 特徴点の設定 図 5 (b) 特徴点の移動 特徴点の設定と移動 Fig. 5 Setting and Moving of feature points. (d)2 値化処理 (e) エッジの抽出 図 3 (f) 人物領域の抽出 人物領域の抽出過程 Fig. 3 Extraction process of human region. 図 6 角度の測定 Fig. 6 Estimation of angle. 距離情報を用いて回帰分析により姿勢推定を行っているも の [3] がある しかし これらの手法では動画撮影に複数 (b)hog 特徴量可視化画像 (a) 入力画像 図 4 HOG 特徴量の抽出 Fig. 4 HOG Extraction. のカメラや特殊なカメラが必要となる 本研究の目的は個 人が自宅でお辞儀習得できる環境を構築することであり 一般的な家庭にある単眼カメラのみを用いるべきである そこで本研究では 大西らの研究 [4] を参考に 物体の形 3. 提案手法 状を表すことが可能な HOG 特徴量 (3.2 節) を用いて 人 3.1 概要 物姿勢推定を行った なお 大西らの研究では 3 次元姿勢 日本でのお辞儀は 挨拶や敬意を表現する行為であり の推定をしているが お辞儀の正確さを評価するためには 相手に対して上体を曲げる動作をする お辞儀をされた側 人物の背の伸び具合などを考慮する必要があるため 本研 は 上体の変化を読み取って お辞儀を受けたと判断する. 究では HOG 特徴量の抽出までのみを行った よって カメラも被験者の正面に設置するべきだが お辞 図 4(a) は入力画像 図 4(b) は入力画像から抽出した 儀の姿勢 角度 時間を取得するためには 被験者の側面 HOG 特徴量を可視化した画像である 節で示した から動画を撮影する必要がある 今回は実環境におけるお エッジ抽出までの処理過程を同様にして行い 入力画像か 辞儀の識別を目的としておらず 個人においてお辞儀を習 らエッジ画像を作成し HOG 特徴量の抽出をする この 得する際の評価システムを構築することが目的である そ ような HOG 特徴量抽出をお辞儀動画の全フレームにおい のためカメラは被験者の側面に設置し 以下の過程でお辞 て行い 得られた値をフレームごとに列挙し 1 つのデー 儀の各種パラメータを取得した. タとしてまとめたものを 姿勢 パラメータとする 背景差分法を用いて人物領域を抽出 角度 時間 パラメータの測定 人物領域の抽出過程を図 3(a) (f) に示す 図 3(a) はお 辞儀動画の 1 フレーム目の画像であり これを入力画像と 以下に説明する手法によって 角度 時間 パラメー タの測定を行う する また 背景画像として図 3(b) を事前に撮影する 入 まず お辞儀の動きを動画の各フレームにおけるオプ 力画像と背景画像から背景差分法によって図 3(c) の差分画 ティカルフロー (3.3 節) で表現する 図 5(a) は人物領域内 像を取得する その後 図 3(d) の 2 値化処理を行い 得ら に特徴点を均一に設定した画像であり 特徴点ごとにオプ れた画像から図 3(e) のエッジ抽出をする 画像のエッジを ティカルフローを算出する オプティカルフローの算出範 矩形で囲み 図 3(f) のような人物領域の抽出を行う 囲を画像全体から限られた特徴点に限定することで 高速 姿勢 パラメータの測定 化とノイズ除去を実現できる また 図 5(b) はお辞儀動 人物の姿勢を推定する手法には 複数のカメラを用いて 作中の特徴点の動きを示したものである. オプティカルフ 身体の形状を推定するもの [2] TOF カメラから得られる ローを用いたトレース処理により 特徴点の動きを求める Information Processing Society of Japan 3
4 HOG HOG HOG(Histogram of Oriented Gradients)[5] HOG (x, y) I(x, y) { fx (x, y) = I(x + 1, y) I(x 1, y) f y (x, y) = I(x, y + 1) I(x, y 1) (1) f x, f y m(x, y). m(x, y) = f x (x, y) 2 + f y (x, y) 2 (2) θ(x, y) = tan 1 f x(x, y) f y (x, y) (3) N c N c N b N b 1 f v v = f v ϵ 2 (ϵ = 1) (4) v 1 HOG Fig. 7 7 Experiment Environment. [7] () t N N (i 0, j 0 ) (p, q) SAD(Sum of Absolute Difference) SAD SAD(p, q) = N 1 N 1 j=0 i=0 f t (i 0 + i, j 0 + j) f t 1 (i 0 + i + p, j 0 + j + q) (5) (p, q) SAD(p, q) (p m, q m ) (p m, q m ) Web Web 2.4m Web 0.9m Web fps. 3.3 [6] [] 3 4
5 2 Table 1 Table 2 1 Criteria of evaluation type of the bow and situation HOG HOG HOG HOG [] Table 3 3 Validation results of posture parameter [] 3 [] ± % HOG 8(a) (b) 8(a) 0.2 8(a) 5
6 情報処理学会研究報告 表 4 角度 パラメータの検証結果 Table 4 Validation results of angle parameter. 会釈 (15 度) 敬礼 (30 度) 最敬礼 (45 度) 全体 平均 標準偏差 表 5 時間 パラメータの検証結果 Table 5 Validation results of time parameter. 会釈 (15 度) 敬礼 (30 度) 最敬礼 (45 度) 全体 平均 標準偏差 (a) の大きい悪いお辞儀 (b) の小さい悪いお辞儀 図 8 悪いお辞儀 (a) 人物領域抽出の失敗例 1 図 9 (b) 人物領域抽出の失敗例 2 人物領域抽出の失敗 Fig. 8 Example of bad bow. Fig. 9 failure of human region extraction. ため システムによって比較的高い姿勢スコアと測定され ことや 現状とは別の手法を考案することで より正確な 客観的評価値と大きなギャップが生じてしまったためだと 人物領域の抽出をし 角度 パラメータのを小さくで 考えられる 図 8(b) のお辞儀では首も背筋も曲がってい きるのではないかと考える るので 姿勢スコアと客観的評価値のギャップは少なくな また 時間 パラメータの検証結果について 表 5 より りは小さいものとなった 以上のことから首の曲がり との全体の平均値が 秒 標準偏差 は客観的評価値を下げる要因として大きいとわかった そ は 秒である この結果から予測 ± 秒 のため HOG 特徴量における首の曲がりに対して重み付け の範囲で 時間 パラメータの測定が可能だと考えられる をすることで 姿勢スコアの測定に首の曲がりも考慮され 大きなが生じていたお辞儀動画を確認したところ を小さくできるのではないかと考える 直立状態時にも被験者が動作しているものが多かった お 角度 時間 パラメータの検証結果と考察 辞儀時間の判定にはオプティカルフロー値を用いており 各お辞儀動画における 角度 時間 パラメータにつ オプティカルフロー値が一定の閾値を越えるとお辞儀時間 いて お辞儀の種類ごとに システムによると目測 と認識され 時間が加算されていく そのため直立状態時 で計測したとそのを求め それぞれで平均値と にも被験者が動作している場合 オプティカルフロー値が 標準偏差を算出した また 3 種類全てのお辞儀における 変化してしまい お辞儀をしていない時間もお辞儀時間に の平均値と標準偏差を算出した 検証結果を表 4 5 加えられ 正確なお辞儀時間を測定できていなかった こ に示す の結果からオプティカルフローの閾値設定方法を再検討す 角度 パラメータの検証結果について 表 4 より測定 値との全体の平均値が 2.82 度 標準偏差は 2.47 度である この結果から予測 2.82 ± 2.47 度の範囲で 角度 パラメータの測定が可能だと考えられる ることで改善が見込める 5. まとめ 本研究では 単眼カメラのみを用いてお辞儀の正確さを の大きいお辞儀動画を確認したところ 図 9(a),(b) 評価するために 姿勢 角度 時間 のパラメータを測 のように人物領域抽出を失敗しているものが多かった こ 定する手法を提案した HOG 特徴量を用いることで 姿 れらの動画では 人物の服装と背景の輝度値が類似してい 勢 パラメータを測定し オプティカルフローを用いるこ たため 正確な背景差分画像が取得できず 人物領域抽出 とで 角度 と 時間 パラメータを測定した また 測 が失敗していた このことから背景差分法の見直しをする 定された各パラメータの評価も行った 姿勢 パラメー 2015 Information Processing Society of Japan 6
7 3 3 [1] ANA [] (2011) [2] Boosting CVIM [ ] Vol 164, pp (2008) [3] CVIM [ ] Vol 177 No16 pp 1-8(2011) [4] HOG 3 MIRU (2008) [5] N.Dalal and B.Triggs Histograms of Oriented Gradients for Human Detection IEEE Computer Vision and Pattern Recognition (2005) [6] J.L.Barron D.J.Fleet S.S.Beauchemin Perfomance of Optical Flow Techniques International Journal of Computer Vision 12-1 pp43-77(1994) [7] D.I.Barnea H.F.Silverman A Class of Algorithm for Fast Digital Image Registration IEEE Trans.on Computers 21,pp (1972) 7
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