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    題名: 基於球員行動的羽球贏家策略
    Badminton winner strategy synthesis based on player position motions
    作者: 陳宜莉
    貢獻者: 郁方
    陳宜莉
    關鍵詞: 自動化程式
    物件偵測
    序列預測
    邏輯規則
    序列分析
    Automation Programs
    Object Detection
    Sequence Prediction
    Logic Rule
    Sequence Synthesis
    日期: 2022
    上傳時間: 2023-09-01 14:52:33 (UTC+8)
    摘要: 利用機器學習技術對球員行為進行分析已在足球和棒球等各種活動中取得成就。我們將這一成就延續到羽毛球領域，旨在系統地綜合球員的動作，從 Youtube 視頻中分析選手對不同對手的勝負策略。模型的學習過程基於可從影片的幀中識別的球員位置（相對於基於跟蹤羽毛球的方法而言），我們使用來自 Youtube 上的國際羽聯官方網站的視頻。
    分析框架包括幾個階段：首先將羽毛球比賽的幀整理成為同一種畫面，然後採用 MASK-RCNN 檢測幀中的雙方球員，以獲得他們的邊界框和坐標。
    另外我們訓練一個 DeepCTRL 模型，根據球員的坐標預測每幀中羽毛球的方向。
    我們使用連續的位置坐標來預測每幀中的羽毛球方向。為了提高精準度，我們採用了DeepCTRL 模型結合了 Bi-LSTM 模型作為方向序列預測的模型，並附加了分數規則，以指導正確分數變化下的學習過程。羽毛球的方向可用於識別擊球幀（基於方向的變化）、獲勝者（下一個發球者）和比賽歷史。然後，我們利用球員在擊球幀中的位置來檢測擊球類型，並進一步識別每個點的勝負擊球序列。為了克服觀察到的擊球序列不足的限制，我們使用 SeqGAN 合成更多的勝負擊球序列，以識別戴資穎對不同對手的策略。最後我們以戴資穎參加奧運會和世界羽聯匯豐銀行總決賽的比賽為例，總結了她對不同對手的勝負策略。
    Systematical analysis on player behaviors that leverages modern machine learning techniques
    has shown great success in various activities such as football and baseball. We continue
    the success to badminton and aim to systematically synthesize player movements to analyze
    their winning/losing strategies against different opponents from Youtube videos. The learning
    process is based on player positions that can be recognized from rather low-resolution
    frames (with respect to approaches based on tracking shuttlecocks), advancing our source
    base in scale to widely available videos, e.g., from the BWF official website on Youtube.
    The analysis framework involves several stages: We first convert frames of badminton games
    into normalized court frames, then adopt MASK-RCNN to detect both players in frames
    to obtain their bounding boxes and coordinates. We train a DeepCTRL model to predict
    directions of the shuttlecock in each frame based on players’ coordinates. Our DeepCTRL
    model combines a Bi-LSTM model as the task base for direction sequence prediction with
    additional rule loss on scores to guide the learning process under correct score changes. The
    directions of the shuttlecock can then be used to identify Shot frames (based on changes of directions),
    point winners (the next starting direction) and match history (direction segments).
    We then use the position of players in shot frames to detect shot type and further identify
    winning/losing shot sequences in each point. To overcome the limitation of insufficient observed
    shot sequences, we synthesize more winning/losing shot sequences with SeqGAN to
    recognize the Tai Tzu Ying’s strategies against different opponents. We take Tai Tzu Ying’s
    games including Olympic games and BWF HSBC Final games as an example and summarize
    our findings in her winning and losing strategies to different opponents.
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    描述: 碩士
    國立政治大學
    資訊管理學系
    109356049
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0109356049
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

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