IPSJ SIG Technical Report Vol.2016-CE-133 No /2/ ,.,,,.,,.,,,.,,,,,., HOG Evaluation System of the Exactness Bow using a Monocular Camer
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1 1 2 1,.,,,.,,.,,,.,,,,,., HOG Evaluation System of the Exactness Bow using a Monocular Camera Abstract: The bow is a symbol of greeting culture in Japan, and it is an important action for smooth communication. People can learn a correct bow by some methods, such as to use textbooks or internet by oneself, and to attend manner lectures. However, the former method has a problem that the learner cannot evaluate the correctness of own bow without an instructor. The latter method has a problem that the manner lectures is not easy to attend. In addition, it takes cost and time. In this paper, we develop a system which evaluates the user s bow from videos taken by a monocular camera, realize an environment that people can learn correct bow by oneself easily. Towards implementation of this system, we defined posture, angle, and time as parameters for evaluation of the user s bow. We developed the system which obtains above-mentioned parameters from videos by a monocular camera. Moreover, we evaluated the accuracy of our proposed system by comparing with the expert s bow evaluation. Keywords: bow learning system Image processing Histogram of Oriented Gradients Optical flow Graduate School of Natural Science & Technology, Kanazawa University 2 Tokyo Healthcare University [1] [2] 1
![2.1 [3] 1 ) ( 3 ) ( 4 ) 3 [3] 3 2(a)](/docs-images/95/123876444/images/2-1.jpg "(c) (a) 15 (b) () 30 (c) (a) 1 Fig.")
2 ( 1 ) ( 2 ) (1) Web (2) (2) [3] 1 ( 1 ) ( 2 ) ( 3 ) ( 4 ) 3 [3] 3 2(a) (c) (a) 15 (b) () 30 (c) (a) 1 Fig. 1 2 Fig. 2 1 Table 1 Process of the bow. (b) Types of the bow. Times of the bow. (c) ( 1 ) 2
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 4 (b) 特徴点の移動 特徴点の設定と移動 Fig. 4 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. ( 2 ) 時間 パラメータ 直立状態から屈体状態までの時間を 屈体時間 屈 体状態でしばらく静止する時間を 静止時間 屈体状 図 5 角度 パラメータの取得 Fig. 5 Obtaining angle parameter. 態から直立状態までに戻る時間を 伸展時間 とし お辞儀の 時間 パラメータと定義する 単位は秒と を目的としておらず 個人においてお辞儀を習得する際の する 屈体時間 静止時間 伸展時間はお辞儀の種類 正確さ評価システムを構築することが目的である そのた ごとに長さが定義されている 表 1 にお辞儀の時間を めカメラは被験者の側面に設置し お辞儀の各種パラメー まとめた タ取得と正確さ評価を行う ( 3 ) 姿勢 パラメータ 首の曲がりや背筋の伸びなどを考慮できるように人 物の上半身のシルエットを姿勢とした お辞儀を直立 3.2 お辞儀のパラメータ取得 人物領域の抽出 時間 (段階 1) 静止時間 (段階 2) 直立時間 (段階 3) 人物領域の抽出過程を図 3(a) (f) に示す 図 3(a) はお に分け 各段階で姿勢を取得し それを 1 つにまとめ 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 たものを 姿勢 パラメータと定義する する また 背景画像として図 3(b) を事前に撮影する 入 その他 礼儀正しいお辞儀の要素として 目線の位置や 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 笑顔 先に挨拶の言葉を言うことが挙げられるが 相手に 像を取得する その後 図 3(d) の 2 値化処理を行い 得ら 良い印象を与えるための追加要素であり 正確さ評価にお 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 ける優先度は高くない 本研究ではお辞儀の基礎的な技術 矩形で囲み 図 3(f) の人物領域抽出を行う を評価することを重視するために これらについては考慮 角度 パラメータの取得 していない また お辞儀は行う人の立場によって違いが まず お辞儀の動きを動画の各フレームにおけるオプ ある 例えば 性別によって違い 男性でも女性でも基本 ティカルフロー (3.3 節) で表現する 図 3(f) の人物領域 的に手の位置は腰の横であるが 女性は腰の前に手を重ね の縦幅を半分にし 人物上半身領域を抽出する その後 ることが礼儀正しいとされる場合もある 職種による違い 図 4(a) のように人物上半身領域内に特徴点 7 点を均一に もあり 旅館の女将やキャビンアテンダントは通常より深 設定し 特徴点ごとにオプティカルフローを算出する オ いお辞儀をする 以上のようにお辞儀の種類は多種多様で プティカルフローの算出範囲を画像全体から限られた特徴 あるが 今回は一般的なユーザを想定し 2.1 節で示した 点に限定することで 高速化とノイズ除去を実現できる 最も基本的なお辞儀を対象とする 図 4(b) はお辞儀動作中の特徴点の動きを示したものであ る. オプティカルフローを用いたトレース処理により 特 3. 提案手法 徴点の動きを求める. 次に上半身の特徴点 7 点の直線近似 3.1 概要 を最小二乗法によって行い 各フレームにおける人物の上 お辞儀は相手の正面に立って行うため カメラの設置場 半身の傾きと直立状態の角度差を求める 角度差が最も大 所も被験者の正面にするべきである しかし お辞儀の角 きくなったフレームを屈体状態と決定し その時の角度差 度 時間 姿勢を取得するためには 被験者の側面から動画 を 角度 パラメータとして取得する 図 5 は屈体状態に を撮影する必要がある 今回は実環境におけるお辞儀評価 おける上半身の傾きを直線で可視化したものである 2016 Information Processing Society of Japan 3
4 情報処理学会研究報告 3.3 オプティカルフロー お辞儀の動きを抽出するために オプティカルフロー [7] を求める オプティカルフローは動物体解析法の 1 つであ り 画像中の輝度情報から動きを分析し 動きベクトルに より物体の運動を表す 本研究ではオプティカルフローを 求める手法としてブロックマッチング法 [8] を用いる (b)hog 特徴量可視化画像 (a) 入力画像 図 6 HOG 特徴量の抽出 Fig. 6 HOG extraction. ブロックマッチング法では画像を一定の大きさの小領 域 (ブロック) に分割し それぞれのブロックの動きベク トルを求める これは各ブロックが前のフレームのどこに 対応するかを探し 対応するブロックの位置の差を動きベ クトルとするものである t フレーム目の N N の大きさ のブロックの動きベクトルを求めるものとする ブロック 時間 パラメータの取得 の左上端の画素の位置を (i0, j0 ) として このブロックの お辞儀が始まってから終わるまでの時間を屈体時間 静 画像と直前のフレームの対応する位置から (p, q) だけずら 止時間 伸展時間に分け 時間 パラメータとして取得 した位置のブロックの画像との差を比較する 本研究では する それぞれの時間の区別にはお辞儀動画のフレーム間 マッチングの評価に画素値の差の絶対値の和 SAD(Sum of の角度変化を用いる フレームごとに現在のフレームと 1 Absolute Difference) を用いた 以下に SAD の式を示す つ前のフレームの角度差を求め 3 フレーム分の角度差を 合計し角度変化値とする 一定の閾値を決め 角度変化値 SAD(p, q) = N 1 N 1 ft (i0 + i, j0 + j) が閾値を上回った時 お辞儀の始まりと決定する そのフ j=0 i=0 レームから角度変化値が閾値を下回るまでを屈体時間 そ ft 1 (i0 + i + p, j0 + j + q) の後にもう一度閾値を上回るまでを静止時間 上回ってか (1) ブロックの位置 (p, q) を変えて SAD(p, q) を計算し そ ら角度の変化がなくなるまでを伸展時間とする れが最小となる (pm, qm ) を動き検出をしたい物体の画像が 姿勢 パラメータの取得 移動したブロックの位置とする 求められた (pm, qm ) と 人物の姿勢を推定する手法には 複数のカメラを用いて の差を求めることで動きベクトルを導く. 身体の形状を推定するもの [4] TOF カメラから得られる 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 [5] がある しかし これらの手法では動画撮影に複数 3.4 HOG 特徴量 お辞儀の姿勢を取得するために HOG 特徴量を求める 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 HOG(Histogram of Oriented Gradients)[9] は画像の局所 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 領域から輝度勾配と勾配強度を取り出す特徴量であり 物 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 体の大まかな形状を表現することができる 以下に HOG そこで本研究では 大西らの研究 [6] を参考に 物体の形 特徴量の算出手順について述べる 状を表すことが可能な HOG 特徴量 (3.4 節) を用いて 人 物姿勢推定を行った なお 大西らの研究では 3 次元姿勢 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 まず 画像の位置 (x, y) における輝度値 I(x, y) として輝 度勾配を次式で求める { 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 fx (x, y) = I(x + 1, y) I(x 1, y) 究では HOG 特徴量の抽出までのみを行った fy (x, y) = I(x, y + 1) I(x, y 1) (2) 図 6(a) は入力画像 図 6(b) は入力画像から抽出した ここで fx, f y はそれぞれ画像の横方向 縦方向の輝度勾 HOG 特徴量を可視化した画像である 節で示した 配を表す 得られた輝度勾配から 勾配強度 m(x, y) とそ 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 の勾配方向を次式によって算出する. らエッジ画像を作成し HOG 特徴量の抽出をする さら に入力画像の人物上半身領域を抽出し その領域のみの HOG 特徴量を使用することにする 体型差や身長差を考 慮するために 人物上半身領域のサイズは一定の大きさに m(x, y) = fx (x, y)2 + fy (x, y)2 θ(x, y) = tan 1 fx (x, y) fy (x, y) (3) (4) 正規化する 正規化には全お辞儀動画の人物上半身領域の 次に算出された輝度勾配画像を Nc Nc 画素からなる 平均サイズを用いる 以上の HOG 特徴量抽出をお辞儀の セルという小領域に分割する それぞれのセル領域におい 段階 1 段階 3 において行い 得られた値を 1 つのデータ て輝度勾配方向の重み付けを行うことにより輝度の勾配方 としてまとめ 姿勢 パラメータとする 向ヒストグラムを作成する さらに各セルで作成した輝度 2016 Information Processing Society of Japan 4
5 N b N b 1 f v v = f v ϵ 2 (ϵ = 1) (5) v HOG (6) Error Max Max 15 1 Error Max 0 Score = { 0 (Error > Max) (1 Error Max ) 9 (Eroor Max) (6) Fig. 7 Experiment environment. 2 Table 2 Types of the bow and situation Web Web 2.4m Web 0.9m Web fps ( 1 ) 3 (3 6 ) ( 2 ) (7 9 ) ( 3 ) (0 2 ) (1) 2 (2) (3) (2) 5
6 3 Table 3 Mean absolute error of each parameter Table 4 4 Accuracy of scoring within a ceration range Fig. 8 Mean absolute error of angle Fig. 9 Comparison of posture (6) Max Max Max (a) (b) (e) (b),(c) (a) (d),(e) (a) 6
7 Table 5 5 Correlation of parameters in objective assessment. 6 Table Correlation of parameters in this system [1] 27(3) pp (2015) [2] C 135(12) pp (2015) [3] ANA [ ] (2011) [4] Boosting CVIM [ ] Vol 164, pp (2008) [5] CVIM [ ] Vol 177 No16 pp.1-8(2011) [6] HOG 3 MIRU (2008) [7] J.L.Barron D.J.Fleet S.S.Beauchemin Perfomance of Optical Flow Techniques International Journal of Computer Vision 12-1 pp.43-77(1994) [8] D.I.Barnea H.F.Silverman A Class of Algorithm for Fast Digital Image Registration IEEE Trans.on Computers 21,pp (1972) [9] N.Dalal and B.Triggs Histograms of Oriented Gradients for Human Detection IEEE Computer Vision and Pattern Recognition pp (2005) 5. HOG
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