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研究生:宋翊誠
研究生(外文):SUNG, YI-CHENG
論文名稱:植基於卷積神經網路之人群計數
論文名稱(外文):Crowd Counting Based on Convolutional Neural Network
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:吳志宏歐陽振森潘欣泰
口試委員(外文):WU, CHIH-HUNGOUYANG, CHEN-SENPAN, SHING-TAI
口試日期:2020-07-15
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:2020
畢業學年度:108
語文別:中文
論文頁數:49
中文關鍵詞:人群計數卷積神經網路卷積通道特徵雙尺度完全卷積神經網路
外文關鍵詞:Crowd countingconvolutional neural networkconvolutional channel featurestwo-scale fully convolutional network
相關次數:
  • 被引用被引用:0
  • 點閱點閱:79
  • 評分評分:
  • 下載下載:27
  • 收藏至我的研究室書目清單書目收藏:0
隨著人口的增加,應用於視訊監控和異常警告的狀況下,在非常密集的人群中進行人群計數是個重要的步驟;因此,人群計數是智慧安全監控中一個重要的研究主題。本論文提出一種具卷積通道特徵之雙尺度完全卷積神經網路架構進行人群計數。我們對於人群影像透過不同大小的卷積核提取人頭特徵,接續進行卷積神經網路模型的建立與人群計數。從ShanghaiTech影像資料庫和UCF_CC_50影像資料庫的實驗結果顯示,我們所提方法在人群計數上有不錯的效果。
With the increase of population, crowd counting in extremely dense crowds is an important step for video surveillance and anomaly warning. Therefore, crowd counting is an important research topic for smart visual surveillance. In this paper, we propose a two-scale fully convolutional network with convolutional channel features for crowd counting. We extract head features by convolution kernel of different sizes from crowd images and then perform crowd counting by using convolutional neural network. Experimental results on ShanghaiTech dataset and UCF_CC_50 dataset are given to illustrate the feasibility of the proposed approach for crowd counting.
摘要...i
ABSTRACT...ii
誌謝...iii
目錄...iv
圖目錄...v
表目錄...vi
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...2
第二章 文獻探討...4
2.1基於模型分析方法...5
2.2基於軌跡集群分析方法...6
2.3基於卷積神經網路之人群計數方法...7
第三章 研究方法...11
3.1人群計數系統...11
3.2人群計數...12
3.2.1基於密度圖的人群計數...12
3.2.2生成人群密度圖與人群計數...13
3.3卷積神經網路...15
3.3.1特徵融合卷積神經網路...15
3.3.2多列與多尺度網路架構...17
3.3.3具卷積通道特徵之雙尺度完全卷積神經網路...18
3.4損失函數...20
第四章 實驗結果...22
4.1實驗環境...22
4.2人群影像資料庫...23
4.3實驗結果與分析...26
4.3.1實驗一...28
4.3.2實驗二...29
4.3.3實驗三...31
4.3.4實驗四...32
第五章 結論與未來工作...35
參考文獻...37

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