結果分析

第五章 結論和未來研究

5.1 結論

本論文以 Kinect 感測器當作輸入端，利用 Kinect 所提供的人體骨架資訊和動 態時間扭曲演算法對人體姿勢做辨識。在實驗方面進行了兩個部份，一個是對於 固定姿勢的辨識，另一個是自訂姿勢的辨識。由前一章節的結果可以看到，當我 們定義了一些固定的姿勢時，同時必須先行告知使用者要如何進行動作，當姿勢 的表達較簡單且一致的情況下，其姿勢特徵的變化也就相對減少，辨識率比較 高。應用層面來說，在概括樣本實驗中，可由一群人設定姿勢鎖，並讓其他用戶 可以根據指示直接使用，符合快速和方便等特性。在自訂姿勢方面，由於姿勢靈 活度大，當某些類別的姿勢過於類似，加上有關節點遮蔽影響等這些 Kinect 自 身存在的問題，辨識率相對較低，但還是能維持在 75%以上的辨識率。自訂姿勢 的應用比起固定姿勢廣泛許多，如同微軟自家推出 Kinect 配合遊戲機的體感遊 戲，另外使用在操作投影片和電視螢幕也是一種應用。前章節中的增量演算法在 現實上為較理想的方式來呼應此應用，玩家在遊戲機或電腦前面可以設定一些屬 於自己的姿勢來進行操作，當在操作時，使用者會重複做出自己設定的姿勢，電 腦就可以在不增加使用者負擔的狀況下將此姿勢紀錄下來並當作新的參考使下 一次的辨識更為準確。

5.2 未來研究

本文使用單一 Kinect 來辨識人體的姿勢。Kinect 所提供的資訊不管是深度影 像或是人體骨架座標，對於傳統的攝影機已經有了非常多的優勢，深度影像可以 快速的將人體和背景分離，骨架資訊可以快速的追蹤人體，讓使用者很容易上 手。未來希望可以嘗試多台 Kinect 來減少因為關節點遮蔽而產生的辨識錯誤，

也可不再限制使用者必須面向 Kinect，另外在演算法方面可以加以改良，嘗試不 同的機器學習方式和擷取特徵方法讓系統辨識率能夠提高。

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