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研究生(外文):Wei-Cheng Chen
論文名稱(外文):Construction Site Surveillance System
外文關鍵詞:clothing recognitionhard hat detectionvest detectiontorso proportions analysis
  • 被引用被引用:1
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本論文主要分為三大部分:影像前處理、特徵擷取及辨識。首先,以網路攝影機拍攝影像後,透過背景相減法擷取移動的前景影像,藉由前景高度動態定位出頭部及軀幹位置,接著再各別抽取色調直方圖、飽和直方圖以及區域二元圖樣(Local Binary Pattern, LBP)作為特徵,再交由支持向量機(Support Vector Machine, SVM)進行分類。實驗結果顯示,本系統可以有效的辨識工地安全帽及工地背心,其準確率分別為97%和93%。

Numerous construction site accidents have happened around the world in recently years. According to the Ministry of Labor, the occurrence rate of severe occupational injury in construction industry is much higher than others. This high risk is primarily caused by the deficiency of the personal protective equipment (PPE). In this thesis, we apply to the technique of body detection and object recognition on PPE checking system to examine whether construction workers are equipped as prescribed or not. As the result, the rate of construction hazard could be reduced.

There are three parts in our system which including image preprocessing, feature extraction and recognition. First, videos of workers are taken by an IP camera. Then, the moving foreground images would be extracted by background subtraction, and the positions of head and body are located by the height of the foreground image. Lastly, the Support vector machine (SVM) is utilized to perform classification on the features which are hue histogram, saturation histogram and local binary pattern (LBP). The experiment results show the system could effectively recognize the safety hats and safety vests with the accuracies of 97% and 93%, respectively.

口試委員會審定書 i
致謝 ii
摘要 iii
論文目錄 v
圖目錄 vii
表目錄 x
第一章、緒論 1
1.1 研究動機與目的 1
1.2 相關研究 2
1.3 論文架構 5
1.4 系統架構及運作流程 5
第二章、影像前處理 7
2.1 背景模型與前景分割 7
2.2 影像型態學 10
2.3 連通體分析 13
2.4 身體部位分析 14
2.4.1 水平投影 14
2.4.2 膚色濾除 15
2.4.3 比例分析 17
第三章、特徵擷取及SVM 20
3.1 HSV基本色彩特徵 21
3.2 紋理特徵 22
3.3 SVM 24
3.3.1 線性SVM 25
第四章、實驗結果與討論 28
4.1 實驗設備環境 28
4.2 系統實驗影片 29
4.3 SVM訓練 31
4.4 系統實作與結果 34
第五章、結論與未來展望 40
參考文獻 42
附錄一 45
附錄二 49
附錄三 53
附錄四 57

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