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Student Number 985202075
Author Chi-Hong Kuo(郭志宏)
Author's Email Address b21113yr@gmail.com
Statistics This thesis had been viewed 546 times. Download 1679 times.
Department Computer Science and Information Engineering
Year 2010
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Monocular-vision pedestrian detection and tracking
Date of Defense 2011-06-29
Page Count 60
Keyword
  • AdaBoost
  • HOG
  • pattern recognition
  • pedestrian detection
  • SVM
  • tracking
  • Abstract Flowing the growth of economics, the amount of vehicles is rapidly increased and then the traffic accidents and consequentially piled up. Thus the development of vehicle collision avoidance system becomes more and more important. In urban areas, there are lots of pedestrians, bicycles, and motorcycles, thus the detection of the pedestrian-like bikes is the most important task. In this study, we proposed a pedestrian detection and tracking system using monocular camera to help drivers avoiding pedestrian traffic collision.
       In the proposed system, we first compute the gradients of image pixels; then decompose every gradient to two adjacent two directions of nine fixed directions. Third, we construct Histograms of oriented gradient (HOG) features from the processed gradients to detect pedestrians. We have constructed thousands of HOG features; we use an AdaBoost strong classifier composed of sixteen weak classifiers to filter out the background and non-pedestrian features, and then use a precise SVM classifier to detection pedestrians based on the remain features. Finally, we use camshift method to find the failed detected pedestrians to achieve a higher detection rate. Overall system’s detection rate can achieve 89% in the simple background case and 70% in the complex background case.
    Table of Content 摘要  ................................................................................................... -ii-
    Abstract................................................................................................. -iii-
    致謝.................................................................................................. -iv-
    目錄................................................................................................. -v-
    圖表目錄................................................................................................. -vii-
    表格目錄................................................................................................. -ix-
    第一章緒論 .................................................................................................. 1
    1.1研究動機 .......................................................................................... 1
    1.2系統概述 .......................................................................................... 1
    1.3論文架構 .......................................................................................... 2
    第二章相關研究 .......................................................................................... 5
    2.1以HOG為特徵的行人偵測 .............................................................. 5
    2.2 以選取特徵來增進 HOG 特徵的辨識能力 .................................. 8
    2.3. HOG結合LBP特徵 ....................................................................... 11
    第三章HOG特徵 ....................................................................................... 14
    3.1HOG統計方式 ............................................................................... 14
    3.2 減輕邊緣效應 ................................................................................ 17
    第四章ROI選取方式 ................................................................................ 19
    4.1 地平線與攝影機高度 .................................................................... 19
    4.2Integral image .................................................................................. 22
    4.3 快速ROI特徵建立 ......................................................................... 23
    第五章分類器 ............................................................................................ 24
    5.1AdaBoost .......................................................................................... 24
    5.1.1 AdaBoost簡介 .................................................................... 24
    5.1.2 弱分類器 ............................................................................ 25
    5.1.3 階層式AdaBoost ................................................................. 26
    5.1.4 AdaBoost訓練 .................................................................... 27
    5.2SVM .................................................................................................. 29
    5.2.1 SVM簡介 ............................................................................ 29
    5.2.2 SVM訓練 ............................................................................ 31
    第六章追蹤 ................................................................................................ 32
    6.1 行人區域群聚 ................................................................................ 32
    6.2反投影 ............................................................................................ 33
    6.3Camshift .......................................................................................... 37
    6.4追蹤結果 ........................................................................................ 38
    第七章實驗結果 ........................................................................................ 40
    7.1 實驗設備與架設環境 .................................................................... 40
    7.2 預先實驗與結果展示 .................................................................... 41
    7.3 不同狀況下的比較 ........................................................................ 47
    第八章 未來展望 ........................................................................................ 54
    參考文獻 ...................................................................................................... 57
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    Advisor
  • Din-Chang Tseng(曾定章)
  • Files
  • 985202075.pdf
  • approve in 1 year
    Date of Submission 2011-07-25

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