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研究生:張正誼
研究生(外文):Cheng-Yi Chang
論文名稱:利用HOG於影像基礎之行人偵測於室內緊急應變
論文名稱(外文):Using HOG for Video-based Human Detection for In-building Emergency Response
指導教授:陳柏華陳柏華引用關係
指導教授(外文):Albert Y. Chen
口試委員:謝尚賢周建成
口試委員(外文):Shang-Hsien HsiehChien-Cheng Chou
口試日期:2015-07-01
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:48
中文關鍵詞:行人偵測方向梯度直方圖多目標行人追蹤行人計算卡爾曼濾波支持向量機影像去背
外文關鍵詞:Human DetectionsHOGMulti-human TrackingHuman CountinKalman FilterSVM classifierBackground Subtraction
相關次數:
  • 被引用被引用:0
  • 點閱點閱:241
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
行人偵測及追蹤在智慧型運輸系統當中是一項重要的應用。行人流量的資料能幫助推估通道容量,而蒐集行人軌跡能夠從中觀測一些行人在空間中的移動傾向。本研究呈現基於影像之自動化行人追蹤及計算方法,預期將應用於室內通道之特性擷取及緊急救災時之情蒐。研究方法利用方向梯度直方圖(Histogram of Oriented Gradient),作為訓練樣本的特徵,進而訓練支持向量機(Support Vector Machine)作為分類器。結合影像去背法(Background Subtraction),本研究採用之計算流程能夠快速且準確偵測行人。而卡爾曼濾波器(Kalman Filter)及匈牙利演算法(Hungarian Algorithm)則於計算流程中幫助追蹤並建立行人軌跡。本研究最後針對數個行人樣本進行驗證,以瞭解本研究所提出之系統效能。結果顯示本研究的自動化行人追蹤及計算系統有潛力應用於室內緊急應變。

Human detection and tracking is an important application in intelligent transportation systems. The data of pedestrian flow could help estimate passage capacity, and collection of pedestrian trajectories could observe the movement tendencies of people passing through the area. In this thesis, we present an approach for video-based automatic people tracking and counting. This approach is expected to be utilized in in-building emergency situations. By extracting the Histograms of Oriented Gradient (HOG) features from the training dataset, the Support Vector Machine (SVM) is trained as the human detector. By combining the HOG detector with Background Subtraction, the detection process could achieve rapid and accurate detections. The Kalman Filter and the Hungarian Algorithm are utilized in this process for the establishment of human trajectory tracking. We test the system on several pedestrian datasets to validate the performance of the proposed system. The automated pedestrian counting system has shown its potential provided the tracking results.

口試委員會審定書 i
誌謝 ii
摘要 iii
ABSTRACT iv
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Research Objectives 2
1.3 Research Flowchart and Thesis Organization 3
CHAPTER 2 LITERATURE REVIEW 5
2.1 Feature Descriptors 5
2.2 HOG-based Detector 6
2.3 Pedestrian Tracking & Trajectories Analysis 7
2.4 Summary 8
CHAPTER 3 METHODOLOGY 10
3.1 Pedestrian Detector 10
3.2 Image Processing 13
3.2.1 Background Subtraction 13
3.2.2 Shadow Removal and Morphological Process 14
3.2.3 Homography Projection 15
3.3 Pedestrian Tracking and Counting 17
3.3.1 Kalman Filter 17
3.3.2 Hungarian Algorithm and People Counting 18
3.3.3 Pixel Value Matching 19
3.4 Preliminary Detection and Tracking Results 20
CHAPTER 4 VALIDATION AND APPLICATION 22
4.1 Experiment Preparation 22
4.1.1 Hardware & Packages 22
4.1.2 Testing Dataset 23
4.2 Detection Results 24
4.3 Tracking and Counting Results 27
4.4 Application 30
4.5 Summary 37
CHAPTER 5 CONCLUSION AND FUTURE WORK 38
5.1 Conclusion 38
5.2 Future Work 38
REFERENCE 40
APPENDIX 45


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