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研究生:陳珮宜
研究生(外文):Pei-Yi Chen
論文名稱:建構在移動物體軌跡的相似度之影片查詢
論文名稱(外文):Video Retrieval based on Similarity of Motion Tracks of Moving Objects
指導教授:陳良弼陳良弼引用關係
指導教授(外文):Arbee L.P. Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
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:2002
畢業學年度:90
語文別:中文
論文頁數:32
中文關鍵詞:視訊物件移動軌跡MPEG-7相似程度計算視訊物件軌跡切割方法相關回饋
外文關鍵詞:motion trackMPEG-7similarity measuremotion track segmentationrelevance feedback
相關次數:
  • 被引用被引用:1
  • 點閱點閱:192
  • 評分評分:
  • 下載下載:19
  • 收藏至我的研究室書目清單書目收藏:1
運動軌跡(motion track)是視訊資料中一個重要的特徵值,它可以表現運動物體在時間與空間上的相對關係,也是內涵式影片查詢中一項重要的索引。擷取運動軌跡的方法,就是將物體在每個畫面的位置連接起來,在壓縮視訊資料內可利用運動向量(motion vector)可推導出來。為了能更充分利用及管理運動軌跡的資訊,在這篇論文中,我們在MPEG-7的運動描述子(motion descriptor)之上,提出了一個新的運動軌跡表示方法,加入了在X-Y平面上表示軌跡的描述,以及在每個軸上對速度趨勢的描述。我們使用一次、二次或三次的多項式來作運動軌跡的表示方法,並使用多項式的係數作為特徵值。在這樣的表示方法之上,我們會定義新的相似度測量方法,分別是在軌跡方面,根據不同次方的曲線的不同特性,例如局部最大/最小值、平滑及延展程度等等,訂定其相似特徵值,以及在速度方面根據不同的速度趨勢變化來訂定相似的程度。我們更進一步提出分段的方法,以方便在這樣的表示法上處理有複雜行為的運動軌跡,並提出索引結構及比對的方法以進行有效率的進行查詢處理。在查詢處理之後,使用相關回饋(relevance feedback)的技巧來調整相似度各個特徵值距離的比重,使用者可以藉由回饋的機制,得到他所想要的查詢結果。實驗結果顯示我們的方法可以達到約85%的精準度,同時在查詢處理上是有效率的,並可以作多段運動軌跡的查詢。
The motion track is an important feature to show the spatio-temporal relationship of a video object. In this thesis, we propose a novel motion track representation based on MPEG-7 motion descriptor. A new descriptor is proposed to represent the motion track in the X-Y plane and the trend of velocity changes. We use the 1st/ 2nd/ 3rd order polynomials to model the trajectory of moving objects and use the associated coefficients as their features. Moreover, a new similarity measure for comparing two motion tracks based on the motion trajectory and velocity differences is proposed. The trajectory is compared by the properties of the polynomials such as the peaks and tangent lines, and the velocity is compared by the different trends such as speeding up and speeding down. Furthermore, the motion track segmentation method is proposed to handle a complicated motion behavior. The indexing and matching algorithm are also considered and the relevance feedback is used to improve the query results. The experiments show the query processing is efficient and the precision is near 85%, which is higher than other approaches.
Abstract II
Acknowledgement III
Contents IV
List of Figures V
List of Tables VI
1. Introduction 1
2. Motion Track Representation 4
2.1. Representation of motion trajectory 4
2.2 Representation of velocities 6
3. Indexing and Query processing 8
3.1 Segmentation 8
3.2 Similarity Measure 9
3.2.1 Similarity features of motion trajectories 10
3.2.2. Velocity similarity 14
3.2.3. Similarity of motion tracks 15
3.3. Indexing 15
3.4. Matching method 17
4. Weight Adjustment by Relevance Feedback 20
4.1. Introduction of relevance feedback 20
4.2. Our approach 21
4.3. Feedback experiments 23
5. Performance Analysis and Experiment Results 25
5.1. Approach overview 25
5.2. Data generation 26
5.3. Performance analysis 27
5.3.1. Feature extraction and index construction time 27
5.3.2. Effectiveness measure: compared with Lee’s work [15] 27
5.3.3. Multiple segment matching 29
6. Conclusion 32
Reference 33
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