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研究生:邱顯峻
研究生(外文):CHIU, HSIEN-CHUN
論文名稱:植基於區域紋理特徵之人群密度估測
論文名稱(外文):Crowd Density Estimation Based on Local Texture Features
指導教授:賴智錦賴智錦引用關係
指導教授(外文):LAI, CHIH-CHIN
口試委員:賴智錦吳志宏潘欣泰歐陽振森
口試委員(外文):LAI, CHIH-CHINWU, CHIH-HUNGPAN, SHING-TAIOUYANG, CHEN-SEN
口試日期:2019-07-26
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:電機工程學系碩博士班
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:2019
畢業學年度:107
語文別:中文
論文頁數:66
中文關鍵詞:人群密度估測紋理特徵增強型強健中心對稱區域三元圖樣支援向量機
外文關鍵詞:Crowd density estimationtexture featuresimproved robust center-symmetric local ternary patternsupport vector machine
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隨著人口密度的增加,人們對於智慧安全監控的需求提高,因此人群密度估測是影像式群眾監控中一個重要的研究主題。本論文提出一種增強型強健中心對稱區域三元圖樣的特徵擷取技術進行人群密度估測。我們對人群影像提取區域紋理特徵,然後通過支援向量機進行模型訓練與辨識。根據 PETS 2009 影像資料集與 Mall 影像資料集的實驗結果顯示,我們所提的方法在人群密度估測上有不錯的效果。
With the increase of population, there is a rising demand for smart visual surveillance. Therefore, crowd density estimation is an important research topic for image-based crowd monitoring. In this paper, we propose an improved robust center-symmetric local ternary pattern for crowd density estimation. We extract texture features from crowd images and then perform recognition by using a support vector machine. Experimental results on PETS 2009 dataset and Mall Dataset are given to illustrate the feasibility of the proposed approach for crowd density estimation.
摘要...i
ABSTRACT...ii
誌謝...iii
目錄...iv
圖目錄...vi
表目錄...vii
第一章 緒論...1
1.1 研究動機與目的...1
1.2 研究方法與架構...1
第二章 文獻探討...4
2.1 基於像素分析之人群密度估測...5
2.2 基於紋理分析之人群密度估測...6
2.3 基於角點分析之人群密度估測...9
2.4 基於深度學習之人群密度估測...10
第三章 研究方法...13
3.1 人群密度估測系統...13
3.2 影像前處理...14
3.2.1 離散小波轉換...14
3.2.2 離散餘弦轉換...16
3.3 特徵擷取...17
3.3.1 Z型區域二元圖樣...17
3.3.2 延展中心對稱區域二元圖樣...19
3.3.3 強健區域二元圖樣...20
3.3.4 增強型強健中心對稱區域三元圖樣...21
3.4 區塊式分割...23
3.5 支援向量機...24
第四章 實驗結果...29
4.1 實驗環境...29
4.2 人群影像資料集...30
4.3 實驗結果與分析...32
4.3.1 實驗一...34
4.3.2 實驗二...36
4.3.3 實驗三...38
4.3.4 實驗四...40
4.3.5 實驗五...42
4.3.6 實驗六...43
4.3.7 實驗七...46
第五章 結論與未來工作...51
參考文獻...53
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