基於機器學習於多深度攝影機架構下之即時籃球動作辨識

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姓名

郭瑋元(Wei-Yuan Kuo) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於機器學習於多深度攝影機架構下之即時籃球動作辨識
(A Real-time Basketball Action Recognition based on Machine Learning Algorithm in Multi-View Environment)

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摘要(中)

人類動作辨識這項技術在電腦視覺及圖形識別等領域一直是重要的研究議題。這項技術可以廣泛應用在如醫學復健、運動動作訓練、電玩遊戲與監控系統等領域裡。在早期的動作辨識相關研究之所以不普及，主要是因為在設備的成本與使用空間需求上不是一般研究者可以負擔得起。但Kinect 體感攝影機的發布改變了這一現象，其低成本的優點使得大量研究者開始投入動作辨識的相關研究領域。
本論文利用 Kinect 裝置搭配由Windows公司推出的Kinect SDK2.0開發套件所提供的25個人體骨架關節點資訊與深度資訊製作出一套人體動作姿勢的辨識系統，用以解決昂貴的傳統深度攝影機所造成的設備成本問題並使用其中的骨架資訊來加強辨識準確率。本論文為了解決複雜的人體動作而產生的自我遮蔽問題和其他錯誤現象所造成的辨識率下降，故我們使用了兩台的Kinect攝影機來做到一個資料來源的互補，這樣即可以大幅降低因可能的遮蔽現象所造成的辨識誤判。之後我們再進行目標物的特徵擷取利用機器學習的方法建立的辨識系統，用以建立我們的資料庫。

摘要(英)

Human action recognition has been an important research in computer vision and computer graphics. It is widely used in entertainment, sports, medical applications and surveillance system. The traditional motion capture equipment is not usually affordable for normal developer. With the reasonable price of Kinect camera, low-cost human motion recognition becomes possible.
In this paper, we use multiple Kinect sensors and Kinect SDK as the tool to build our human action recognition system. This solves the problem of action recognition equipment costs. Using multiple Kinect cameras to solve the judging and correction error problems (such as self-occlusion and image noise...etc.) and using machine learning method to classified our features, it can make our recognition result with higher performance.
In our methods, we also have a detection of basketball to prevent that the subject is without ball, it makes our works more reasonable. Above of all, this paper have the action recognition rate to be more than 90% in real-time usage from three of the trained behaviors, i.e. right-hand dribble, left-hand dribble, and shooting behaviors.

關鍵字(中)

★ 人類動作辨識
★ 機器學習

關鍵字(英)

★ action recognition
★ multiple Kinects
★ Kinect SDK

論文目次

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章緒論 1
1.1　研究背景 1
1.2　研究動機與目的 5
1.3　論文架構 6
第二章　相關研究環境介紹 7
2.1　Kinect體感攝影機 7
2.1.1　Kinect感測器之硬體規格 8
2.1.2　現有深度攝影機比較 9
2.1.3　Kinect軟體開發工具介紹 10
2.2 Kinect相關工具 13
2.2.1　Kinect Studio 13
2.2.2　Visual Gesture Builder 18
第三章　動作辨識相關技術介紹 19
3.1　動作辨識技術簡介 19
3.2　影像低階特徵擷取 20
3.2.1　色彩特徵 20
3.2.2　紋理特徵 21
3.2.3　形狀特徵 21
3.2.4　骨架特徵 22
3.2.5　深度特徵 23
3.3　機器學習之相關介紹 24
3.3.1　隨機森林 Random Forest 25
3.3.2　支持向量機 SVM 28
第四章提出之動作辨識技術 31
4.1　系統架構 31
4.2　VGB訓練 33
4.3　特徵擷取與訓練 35
4.3.1　VGB訊號 35
4.3.2　相對位置特徵 37
4.3.3　籃球輪廓偵測 39
4.3.4　特徵訓練 41
第五章實驗結果與分析討論 43
5.1　實驗環境介紹 43
5.2　實驗結果與討論 44
第六章　結論與未來展望 65
參考文獻 66

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指導教授

張寶基(Pao-Chi Chang)

審核日期

2016-11-8

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