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研究生:凌誌鴻
研究生(外文):Zhi-Hong Ling
論文名稱:應用於智慧型家庭監視系統之物件追蹤及事件偵測
論文名稱(外文):Object Tracking and Event Detection for Intelligent Home Surveillance
指導教授:林嘉文林嘉文引用關係
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
論文出版年:2005
畢業學年度:93
語文別:英文
論文頁數:58
中文關鍵詞:物件追蹤事件偵測家庭監視系統
外文關鍵詞:object trackingevent detectionhome surveillance
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智慧型家庭監控主要包含物件追蹤和事件偵測兩種技術。對物件偵測技術而言,particle filter和mean shift是最有名的兩個方法。Mean shift是效率較高的方法,但是有錯誤蔓延的問題。Particle filter可靠性較高,目標候選點愈多結果就會愈準確,但卻會增加時間複雜度。
在本論文中,我們提出結合物件切割的particle filter,其方法結合了以運動為基礎的物件切割法和particle filter的技術。以物件切割來說,估計總體運動參數可以分辨物體運動和攝影機運動,得到較粗略的物體遮罩。接下來,使用物件分類演算法自動分離個別的物件。藉由以高斯模型為主的切割結果,得到目標物大致的資訊,再利用particle filter與較少的目標候選點更新追蹤結果。對事件偵測的部份,我們針對跌倒事件,從追蹤結果中擷取特徵值。根據物體相對於攝影機的移動方向,使用不同的臨界值偵測跌倒事件。
An intelligent home surveillance mainly consists of object tracking and event detection. For object tracking, particle filter and mean shift are well-known methods. Mean shift is an efficient method, but Error propagation is a serious problem of mean shift. Particle filter is a robust tracking method and more target candidates would make the result more robust, but it would increase computation complexity.
In this thesis, we propose a segmentation-assisted particle filter for object tracking. Our method combines motion based segmentation and particle filter. For object segmentation, global motion parameters are estimated to distinguish local object motions from camera motions so as to obtain a rough object mask. Subsequently, an object clustering algorithm is used to automatically separate the individual video objects iteratively. Based on the rough segmentation result, we use particle filter with few target candidates to refine the tracking result. For event detection, we extract three features of each object for identifying and locating fall incidents. Our experiments show that the proposed method can correctly detect fall incidents in real time.
Chapter 1 3
Introduction 3
Chapter 2 8
Overview of Object Tracking 8
2.1 Object Representation 8
2.2 Similarity Measure 10
2.3 Particle Filter 10
2.4 Mean Shift 14
2.5 Hybrid Tracker Combining Mean Shift and Particle Filter 17
Chapter 3 19
Proposed Object Detection and Tracking 19
3.1 Initial Object Detection Using an Omni-Directional Camera 21
3.2 Segmentation-Assisted Particle Filter 25
A. Compressed-domain object segmentation 26
B. Label Set Method 29
C. Similarity Measure 31
D. Refinement of object tracking result using a particle filter 31
3.3 Experimental Results 33
Chapter 4 43
Fall Incident Detection for Intelligent Home Surveillance 43
4.1 Feature-Based Fall Incident Detection 44
4.1 Experimental Results 49
Chapter 5 54
Conclusions and Future Works 54
References 55
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