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研究生:周雍傑
研究生(外文):Yung-Chieh Chou
論文名稱:遊戲視訊分析:事件偵測及精彩片段偵測與預測
論文名稱(外文):On Broadcasted Game Video Analysis: Event Detection, Highlight Detection, and Highlight Forecast
指導教授:朱威達
指導教授(外文):Wei-Ta Chu
口試委員:陳敦裕陳煥宗林彥宇朱威達
口試委員(外文):Duan-Yu ChenHwann-Tzong ChenYen-Yu LinWei-Ta Chu
口試日期:2015-07-24
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
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:2015
畢業學年度:103
語文別:英文
論文頁數:49
中文關鍵詞:遊戲直播影片事件偵測精彩視訊偵測精彩視訊預測基因演算法線性指數平滑法
外文關鍵詞:Broadcasted game videoevent detectionhighlight detectionhighlight forecastgenetic algorithmlinear exponential smoothing
相關次數:
  • 被引用被引用:1
  • 點閱點閱:334
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  • 下載下載:10
  • 收藏至我的研究室書目清單書目收藏:1
由於線上遊戲直播平台的興起,如何有效率地存取這些遊戲影片成為重要的議題。熱門的串流遊戲影片開創了前所未有的市場商機,並造就了許多技術的挑戰。在本篇論文中,我們由兩種層面實現有效率的影片瀏覽:遊戲事件偵測以及遊戲精彩片段偵測。在遊戲事件偵測中,藉由偵測遊戲中重要畫面所出現的關鍵文字並連結時間標記,搭配使用者介面方便遊戲影片的導覽。在遊戲精彩片段偵測中,我們融合了影像的視覺資訊、遊戲事件資訊、以及串流平台中觀眾行為的資訊,建立了兩種不同模式的模組,分別為基於生理情緒模組(arousal model)以及基於數據分析模組(SVM model)。我們並以基因演算法(genetic algorithm)選擇最佳的影片長度,最後將這些精彩片段串聯,便可有效呈現影片中重要之片段。在促進串流平台頻寬調整研究上,我們提出遊戲影片精彩片段預測方法,藉此在即將發生重要片段時,系統能夠規劃給予更多的頻寬,綜合實驗分析結果,我們從各角度展現出以上這些方法的效果及優點。
Efficient access of broadcasted game videos is urgently demanded due to the emergence of live streaming platforms. The popularity of game video streaming builds a big market showing commercial potentials and arising many technical challenges. In this thesis, we facilitate efficient access from two aspects: event detection and game highlight detection. By recognizing designated text displayed on screen when important events occur, we associate game events with time stamps, and accordingly develop an interface to facilitate direct access. For highlight detection, we jointly consider visual features, event features, and viewer’s behaviors to construct two highlight models, i.e., the psychophysiological approach based on the arousal model and the data-driven approach based on support vector machine (SVM). In addition, we propose an optimal subset selection based on the genetic algorithm for video sub-segment selection. The concatenated highlight segments enable compact game video presentation. To facilitate adaptive bitrate streaming, a highlight forecast model is built so that the streaming system can allocate higher bitrate for more important segments on the fly. Comprehensive experiments demonstrate effectiveness of the proposed methods from various perspectives.
Chapter 1 INTRODUCTION 1
1.1 Motivation 1
1.2 System Overview 3
1.3 Contributions 5
1.4 Thesis Organization 6
Chapter 2 RELAED WORKS 7
2.1 Live Streaming Systems 7
2.2 Game Video Analysis 8
2.3 Video Event Detection and Summarization 8
2.4 Summary 10
Chapter 3 AUTOMATIC TEXT BROADCAST 11
3.1 Event Text Detection 11
3.2 Game Event Recognition 13
3.3 Text Broadcast Generation 15
Chapter 4 HIGHLIGHT DETECTION AND FORECAST 16
4.1 Feature Design and Extraction 16
4.1.1 Visual Feature Extraction 16
4.1.2 Event Feature Extraction 19
4.1.3 Chat Feature Extraction 19
4.2 Highlight Detection Models 22
4.2.1 Arousal Model 22
4.2.2 SVM Model 24
4.2.3 Optimal Subset Selection 25
4.3 Highlight Forecast Model 30
4.3.1 Exponential Smoothing Forecast Model 31
Chapter 5 EXPERIMENTAL RESULTS 33
5.1 Dataset and Experimental Setup 33
5.2 Evaluation Results of Text Broadcast 34
5.3 Evaluation Results of Highlight Detection 35
5.4 Evaluation Results of Highlight Forecast 42
Chapter 6 CONCLUSION AND FUTURE WORKS 43
6.1 Conclusion 43
6.2 Future Works 44
REFERENCES 45
APPENDIX 49
PUBLICATION LIST 49

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