[2][3] [4] [5] 2.2 [6] SVM [7] [8] c 2013 Information Processing Society of Japan 2
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1 1,a) 1,b) 1,c) 1,d) 1 Research for automatic tennis play recognition and recording based on motion analysis Antoku Chihiro 1,a) Kashima Masayuki 1,b) Sato Kiminori 1,c) Watanabe Mutsumi 1,d) Abstract: In the sports video analysis based on motion analysis, play action recognition is one of the important problem. The objective was to create automatically recording for tennis game analysis by automatically recognizing the play contents. First, the number of forehand-backhand and the hitting position of player were targeted. As motion recognition method, The motion recognition method based on the trajectory estimation of ball that does not depend on court condition and individual difference in behavior is proposed. First, after detecting player and ball from the movie, the trajectory of ball is estimated by using a motion dynamic model. The intersection point of the trajectories is obtained based on before and after hitting ball, forehand-backhand is judged based on the intersection point exists on the right or left side of player. Also the hitting position of player is judged based on court recognition using planar projective transformation. these judge result is recording as a result. Results were evaluated by the movie in the outdoor tennis court, experimental results have show the effectiveness of the proposal method. Keywords: movie analysis, sports video analysis, tennis, action recognition, trajectory estimation 1. 1 Graduate School of Science and Engineering, Kagoshima University a) k @kadai.jp b) kashima@ibe.kagoshima-u.ac.jp c) kimi@ibe.kagoshima-u.ac.jp d) mutty@ibe.kagoshima-u.ac.jp 1 [1] c 2013 Information Processing Society of Japan 1
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4 6 8 1 ( ) 83 ( ) 74 ( ) 0 ( % ) fore back court out court in fps ( ) = (1) c 2013 Information Processing Society of Japan 4
5 情報処理学会研究報告 図 12 図 10 バック判定例 ショット検出例 (バック) 表 3 選手位置判定結果 表 2 フォア バック判定結果 検出できたショット数 (回) バック 計 バック 計 判定できたショット数 (回) 判定成功数 (回) 判定率 ( % ) 判定成功数 (回) 判定率 ( % ) 図 13 図 11 フォア フォア コート認識結果例 フォア判定例 例を図 12 に示す 判定率 = 判定成功数 100 検出できたショット数 (2) 4.3 選手位置判定結果 第 4.2 節で判定できたフォア バックについて 選手位 置判定の結果を表 3 に示す 判定率は式 3 のように求めて いる またコート認識の結果例を図 13 に示す この結果 から選手位置判定を行い コート外判定の結果例を図 14 コート内判定の結果例を図 15 に示す 判定率 = 判定成功数 100 判定できたショット数 c 2013 Information Processing Society of Japan (3) 図 14 選手位置判定例 (コート外) 5
6 情報処理学会研究報告 図 15 選手位置判定例 (コート内) 図 16 ショット検出失敗例 (影誤検出) 5. 考察 5.1 ショット検出に関して ボール検出結果を用いて 89.2% の精度でショット検出す ることができたが 図 16 に示すように選手の影をボールと して誤検出してしまうという失敗例があった ショット検 出のためには打つ前のボール群と打った後のボール群が必 要となるが 選手の影を誤検出することで打った後のボー ルを検出できず ショット検出することができなかった これは選手の影を正確に抽出することができず小さい領域 となってしまい 選手でなくボールとして検出してしまっ たことが原因である ボール検出時には HSV 色情報によ 図 17 ショット検出失敗例 (ラケット誤検出) る絞込みも行っているが ボールとコート地面が類似した 色となっているため絞込むことができなかった 解決策と でも 90.5% の精度でフォア バックを判定することができ しては 検出処理時に影領域を考慮すること または図 16 たが 図 18 に示すように軌跡を結ぶことができないとい から分かるように 誤検出してしまった時のボールの動き う失敗例があった これは 図 19 の矢印で示す箇所つま が数フレーム近い位置に集まるためその場合は例外処理を りバウンド後からボールが選手領域と完全に重なってしま 行うという条件を加えることなどが考えられる また 図 い検出できなかったことが原因である 図 20 に示すよう 17 に示すようにラケットをボールとして誤検出してしまう にショット検出時は打つ前と打った後のボールがあれば検 という失敗例もあった 同様に選手の足元をボールとして 出できたが フォア バック判定時には打つ前のボールの 誤検出してしまうという失敗例もあった これらは選手領 中でもバウンド後のボールがなければ軌跡の交点を求める 域を検出する際に ラケットや足元を除いてしまい実際の ことができず判定することができなかった 現在は人物領 選手領域全体を検出することができなかったことが原因で 域付近のボールを全て除外しているが ボールの移動量や ある 実際の選手領域の一部を検出することができなかっ 移動方向の考慮などボールの検出処理を改善することで解 たことで その除いた一部を小さい領域としてボールと誤 決できると考えられる 検出してしまったということである 現在は動体領域に基 づいた選手の検出処理を行っているため 選手の動きが少 5.3 選手位置判定に関して なかった時に正確な人物領域をとることができない場合が 平面射影変換に基づいたコート認識を用いて 97.0% の あるが 追跡処理を加えるなど選手の検出処理を改善する 精度で選手位置 (コート内外) の判定をすることができた ことで解決できると考えられる が 図 21 に示すように選手位置はコート外であるが誤って コート内と判定してしまうという失敗例があった これは 5.2 フォア バック判定に関して ボールの軌跡推定を用いることでコート状態が悪い環境 c 2013 Information Processing Society of Japan 人物の足元が正確に検出できなかったことが原因である 現在は動体領域に基づいた人物の検出処理を行っているた 6
![バックの回数と打った時の選手位置のみを記録対象と しているが 今後はネットプレーへの応用などにより記録 対象を増やしていく 図 19 バウンド時のボール検出失敗 参考文献 [1] [2] [3] [4] [5] [6] 図 20 判定失敗時のショット検出 [7] め 選手の動きが少なかった時に正確な人物領域をとるこ とができない場合があるが 追跡処理を加えるなど人物の](/docs-images/93/113901869/images/7-1.jpg "検出処理を改善することで解決できると考えられる c 2013 Information Processing Society of Japan [8] 北原格 大田友一 金出武雄 多視点映像の融合によるス ポーツシーンの自由視点映像生成 電子情報通信学会技 術研究報告.PRMU vol.100 no.633 pp.")
7 情報処理学会研究報告 図 18 フォア判定失敗例 図 21 選手位置判定失敗例 6. おわりに 本研究では コート状態や動作ごとの個人差に依存しな い 軌跡推定を用いた動作認識手法を提案し 有効性を示 すことができた また分析結果を記録するシステムを作成 した 現在の記録対象はフォア バックの回数 打った時 の選手位置となっている 今後の課題として 検出 判定時に失敗した原因を解決 するために検出手法の改善が挙げられる また現在はフォ ア バックの回数と打った時の選手位置のみを記録対象と しているが 今後はネットプレーへの応用などにより記録 対象を増やしていく 図 19 バウンド時のボール検出失敗 参考文献 [1] [2] [3] [4] [5] [6] 図 20 判定失敗時のショット検出 [7] め 選手の動きが少なかった時に正確な人物領域をとるこ とができない場合があるが 追跡処理を加えるなど人物の 検出処理を改善することで解決できると考えられる c 2013 Information Processing Society of Japan [8] 北原格 大田友一 金出武雄 多視点映像の融合によるス ポーツシーンの自由視点映像生成 電子情報通信学会技 術研究報告.PRMU vol.100 no.633 pp (2001) 松本圭介 須藤智 斉藤秀雄 小沢慎治 サッカー放送に おける視点選択のための多視点画像の統合によるボール追 跡 電気学会論文誌.C vol.121 no.10 pp (2001) 西田絵美子 久本賢 ラシキア ジョージ サッカー戦 術解析支援システムの開発 第 8 回画像センシングシン ポジウム講演論文集 pp (2002) 浅野敏郎 佐藤邦彦 浜野博行 原肇 テニススウィ ングの解析と定量評価 精密工学会誌 vol.73 no.2 pp 甲斐桃子 小沢慎治 画像処理によるテニスのダブルス の試合解析 電子情報通信学会技術研究報告 PRMU vol.108 no.46 pp 宮森恒 映像と音響情報の協調による内容検索のための テニス動作自動注釈付け 電気情報通信学会論文誌 D-II vol.j86-d-ii no.4 pp (2003) Guangyu Zhu Changsheng Xu Wen Gao & Oingming Huang Action Recognition in broadcast tennis video Proceedings of 18th International Conference on Pattern Recognition vol.1 pp (2006) 大和淳司 大谷淳 石井健一郎 隠れマルコフモデルを 用いた動画像からの人物の行動認識 電子情報通信学会 論文誌 D-II vol.j76-d-ii no.12 pp (1993) 7
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