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論文名稱(外文):The Study of Dynamic Object Detection and Tracking with PTZ Camera
指導教授(外文):Chu-Sing Yang
外文關鍵詞:PTZ CameraDynamic DetectionObject TrackingLocal Joint Image Group
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為了能讓PTZ攝影機擁有動態偵測與自主追蹤物體的能力,本論文提出區域交集影像群組(Local Joint Image Group)的構想來建立適用於PTZ攝影機的背景模型,其中搭配使用尺度不變特徵轉換(Scale Invariant Feature Transform, SIFT)與RANSAC(Random Sample Consensus)這兩種方法組成影像縫合(Image Stitching)的技術,藉以將離散的背景空間模擬成連續的空間。並使用高斯混合模型(Gaussian Mixture Model, GMM)建立背景配合背景相減法將前景移動物體擷取出來,輔以被追蹤物體的HSV(Hue-Saturation-Value)顏色直方圖與區域二元圖樣(Local Binary Patterns, LBP)紋理特徵整合粒子濾波器(Particle Filter)演算法來進行追蹤以達成本論文的目標。
Object tracking is one of challenging research areas in Computer Vision. In the past, most of the approaches are based on the static cameras. Sometimes, static cameras are improper for some scene because the limitation of FOV. For this reason, the studies of PTZ cameras are increasing recently. However, the characteristic of dynamic FOV accompanies some problems such as background construction. For practical, PTZ cameras usually have to detect moving object statically, then start dynamic tracking procedures.
For the ability of PTZ cameras which could detect and track objects dynamically, we propose the Local Joint Image Group idea to build the suitable background for PTZ cameras. This approach uses image stitching technique including SIFT(Scale Invariant Feature Transform) and RANSAC(Random Sample Consensus) to simulate a continuing background space. Besides, we adopt GMM(Gaussian Mixture Model) and background subtraction to extract the moving objects from images. Finally, we use innovative tracking strategy which integrates particle filter algorithm with HSV(Hue-Saturation-Value) and LBP(Local Binary Patterns) features to reach our goal.
In the experiments, we test different background construction methods for PTZ cameras and made the validity comparison of background subtraction. Moreover, we survey different tracking methods and made a comparison between them. The results show the effectiveness of the proposed method.
摘要 I
Abstract III
誌謝 V
目錄 VI
圖目錄 VIII
表目錄 XI
第一章 序論 1
1.1 研究背景 1
1.2 研究目的與方法 2
1.3 章節概要 2
第二章 相關研究 3
2.1 移動物體偵測 3
2.1.1 連續影像相減法 3
2.1.2 背景相減法 4
2.1.3 區塊匹配法 6
2.1.4 光流法 7
2.2 影像縫合 8
2.3 物體追蹤 10
第三章 PTZ攝影機動態偵測與追蹤方法 14
3.1 架構與流程 14
3.2 建立背景模型 16
3.2.1 背景影像擷取 16
3.2.2 高斯混合模型 17
3.2.3 區域交集影像群組 19
3.3 物體追蹤 32
3.3.1 HSV顏色直方圖 32
3.3.2 區域二元圖樣直方圖 34
3.3.3 粒子濾波器 36
3.3.4 相似度函數 40
第四章 實驗結果 42
4.1 背景模型實驗 42
4.1.1 實驗一 42
4.1.2 實驗二 43
4.1.3 實驗三 44
4.2 物體追蹤 45
4.2.1 實驗一 45
4.2.2 實驗二 49
第五章 結論與未來的研究方向 55
5.1 結論 55
5.2 未來研究方向 55
參考文獻 57
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