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研究生:李家昶
研究生(外文):Chia-chang Li
論文名稱:針對電視轉播的棒球比賽之投手球路辨識
論文名稱(外文):Baseball Pitch Recognition for Broadcast Television
指導教授:林嘉文林嘉文引用關係
指導教授(外文):Chia-Wen Lin
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:95
語文別:英文
論文頁數:35
中文關鍵詞:隱藏式馬可夫模型視訊分析運動棒球球路軌跡
外文關鍵詞:sportvideo analysisbaseballpitchtrajectoryHidden Markov Models
相關次數:
  • 被引用被引用:0
  • 點閱點閱:476
  • 評分評分:
  • 下載下載:78
  • 收藏至我的研究室書目清單書目收藏:4
多媒體與娛樂工業的進步,其中夾帶了與多影音串流與數位內容,也因此出現了管理與存取大量影音資料議題的新挑戰。在公共與商業電視節目中,運動比賽轉播佔了很大的比例。觀眾在視訊擷取、儲存、傳送與視訊處理能力上的需求也日益增加。本篇論文的目的為根據棒球比賽中,投手投出的棒球軌跡,分辨出投球的球路。我們提出了一個分辨球路的方法,分析每種球路軌跡中時間與空間上的特性。根據我們的觀察,每種球路在球速、軌跡、加速度與軌跡形狀上都有其不同的特性。 我們利用隱藏式馬可夫模型建立每一種球路時間上的特性,並且利用統計方法分析球路的加速度與軌跡形狀增加準確率。實驗顯示,在6場的棒球比賽中的195個投球軌跡,證明我們所提出的方法架構是有效且可靠,每一種球路的分辨率皆在0.8以上.
Advances in the multimedia and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Sport broadcasts constitute a major percentage in the total of public and commercial television broadcasts. The growing demands of the viewers require advances in video capturing, storage, delivery and video processing ability. This paper aims to recognize the baseball pitches from the baseball game video captured from broadcast television. We proposed a novel baseball pitch recognition approach which analyzes the trajectories in their spatial and temporal domain. According to our observations, each pitch has its own characteristics in speed, trajectory, acceleration and shape. We model the temporal information for each pitch by using Hidden Markov Models, and use the fundamental statistical approach by using the features acceleration and the shape of the trajectory to improve the precision. The experimental results on 195 trajectories in 6 baseball games confirm that the proposed framework is very effective and reliable and the precision of each pitch is more than 0.8.
1. Introduction 4
2. Characteristics of Baseball Pitches 6
3. Feature Extraction and Classification 9
3.1 Speed Feature 9
3.2 Trajectory Feature 11
3.2.1 K-Means Clustering 13
3.2.2 Hidden Markov Model (HMM) 15
3.3 Acceleration Feature 16
3.3.1 Bayesian Decision Theory 19
3.4 Shape Feature 20
3.4.1 Point Distribution Models 20
4. Experimental Results 26
5. Conclusion and Future Work 32
6. References 33
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