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研究生:聞浩凱
研究生(外文):Hao-Kai Wen
論文名稱:半管運動電視轉播影片之事件偵測技術
論文名稱(外文):Event Detection in Halfpipe Sports Broadcasting Videos
指導教授:吳家麟
指導教授(外文):Ja-Ling Wu
口試委員:陳文進胡敏君鄭文皇
口試委員(外文):Wen-Chin ChenMin-Chun HuWen-Huang Cheng
口試日期:2014-07-26
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:22
中文關鍵詞:半管運動影片事件偵測動作辨識
外文關鍵詞:halfpipevert rampsports videoevent detectionaction recognition
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本文中,一個低成本和高效率的系統性自動分析半管運動轉播影片方法被提出來。除了場地顏色覆蓋比率,我們發現玩家區域使用顯著目標偵測機制可面對半管運動影片模糊場景的挑戰。此外,基於現有的MPEG 壓縮的影片原生的運動向量(motion vector)我們提出一種可用於偵測旋轉(spin)事件的新穎且有效的方法。實驗結果表明,該系統能有效在半管影片中識別難以被檢測到的旋轉(spin)和滑行(grind)等事件。

In this work, a low-cost and efficient system is proposed to automatically analyze the halfpipe sports broadcasting videos. In addition to the court color ratio information, we find the player region by using salient object detection mechanisms for facing the challenge of motion blurred scenes in HP videos. Besides, a novel and efficient method for detecting the spin event is proposed on the basis of native motion vectors existing in MPEG compressed video. Experimental results show that the proposed system is effective in recognizing the hard-to-be-detected spin and grind events in halfpipe videos.

口試委員會審定書i
誌謝ii
摘要iii
Abstract iv
1 Introduction 1
1.1 What is a Halfpipe? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 What is a Trick? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 How to Learn a Trick . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 The Problem of Watching Halfpipe Broadcasting Videos . . . . . . . . . 5
1.5 How to Solve Watching Halfpipe Broadcasting Videos Problems . . . . . 6
1.6 Challenges of Event Detection in Halfpipe Videos . . . . . . . . . . . . . 6
2 Related Work 7
3 Proposed Method 9
3.1 Highest Point Detection and Trick Segmentation . . . . . . . . . . . . . . 9
3.1.1 Noise Elimination . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.2 Peak and Valley Detection . . . . . . . . . . . . . . . . . . . . . 10
3.2 Player Detection and Tracking . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Spin Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Grind Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 Results and Discusion 13
4.1 Cut Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 Highest Point Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.3 Spin Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.4 Grind Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Conclusion and Future Work 19
Bibliography 21

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