自己アピール資料_水野由斗.pdf

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スライド1〜3枚目:　大学三年次に開かれた複数大学合同の研究発表会にて発表した内容です。韓国グループTWICEのメンバーを機械学習で識別させたものです。動画から顔を検出し、検出された顔を切り抜き顔画像として保存します。保存された顔画像をメンバーごとに振り分けて機械学習を行います。精度は約60%ほどでした。過学習が生じたことや、顔の傾き画像への対応がないため、今回のような精度になったと考察しました。今後の課題として、学習させる顔画像の枚数の増加と顔の傾き画像への対応をあげました。スライド4枚: 現在は、オリンピック競技であるホッケーの映像分析を取り組んでいます。弊研究室は、画像処理や映像分析を中心に研究を行っており、その中でもスポーツ分野に力を注いでいます。あまり研究が進められていないかつ、オリンピック競技でもあるスポーツは何かと考え探し出したのが陸上ホッケーでした。分析アプリを作成する上で何を分析すればいいのか主観でしか判断できなかったため、現在プロや学生が使っている動画のタグつけアプリを模範しようと考えました。 Flaskを使って実装しています。試合映像を入力し、シュート、ドリブル、ゴールなど、シーンごとに映像を切り抜き、保存や編集の機能を実装しました。今は、動画のプレビュー機能の追加を目標に実装しています。スライド6〜8枚目: 冒頭で説明した研究会にて質疑応答の場面で使えるような質問対策スライドです。

Ingeniería

愛知工業大学
澤野研究室
3年水野由斗
機械学習を用いたTWICEメンバーの
自動識別手法の提案
メンバーの多さアジア圏出身
見分けがつきづらい
機械学習で顔画像の識別

愛知工業大学
澤野研究室
3年水野由斗
サナ
ダヒョン
ジヒョ
モモ
ナヨン
ジョンヨン
ツウィ
ミナ
チェヨン
機械学習ならすぐにTWICEのメンバー分類できる?
機械学習を用いたTWICEメンバーの
自動識別手法の提案
愛工大澤野研 3年水野由斗
提案手法
動画から顔検出顔画像の切り抜きメンバーごとに機械学習

愛知工業大学
澤野研究室
3年水野由斗
実験結果
今後の課題:
データ枚数の増加，顔の傾き画像の対応
学習用データ枚数 1,711
訓練用データ枚数 1,201
検証用データ枚数 510
識別精度(%) 56.47
機械学習を用いたTWICEメンバーの
自動識別手法の提案
愛工大澤野研 3年水野由斗

愛知工業大学
澤野研究室
3年水野由斗
現在取り組んでいる研究:
あホッケー分析アプリの作成
試合映像入力映像の切り抜きシーンごとに保存，編集，削除
シュートシーン
ドリブルシーン
ゴールシーン
愛知工業大学
澤野研究室
3年水野由斗

愛知工業大学
澤野研究室
3年水野由斗
質問対策
顔検出の手法
※Jiankang Deng. Retinaface: Single-Shot Multi-Level Face Localization in the Wild. 2020
※
①動画をフレームで分割
②フレームごとにRetinafaceを用いた顔の検出

質問対策
顔検出結果
メンバー検出枚数
ダヒョン 218
ジョンヨン 118
ジヒョ 287
ミナ 177
モモ 245
ナヨン 198
サナ 146
ツウィ 134
チェヨン 189

質問対策
効率よく
高い精度を得られる
画像分類モデル
※Mingxing Tan. Ef
fi
cientNet: Rethinking ModelScaling for Convolutional Neural Networks. 2019
機械学習E
ffi
cientNetについて
※

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