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研究生:陳韋成
研究生(外文):Wei-Cheng Chen
論文名稱:工地安全監控系統
論文名稱(外文):Construction Site Surveillance System
指導教授:丁肇隆丁肇隆引用關係
口試委員:呂承諭張信宏
口試日期:2015-06-12
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:工程科學及海洋工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:60
中文關鍵詞:服裝辨識安全帽偵測背心偵測人體比例分析
外文關鍵詞:clothing recognitionhard hat detectionvest detectiontorso proportions analysis
相關次數:
  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:0
近年來工地事故頻傳,營造業之重大職業災害發生比率,相較於其他行業更為嚴重。其中,工地人員裝備安全配戴不齊為造成嚴重傷害的主要原因。為了避免悲劇一再發生,藉由人體偵測及物件辨識技術,判定勞工進入工地時是否按規定著裝,以達到更全面性的安全檢查。
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
ABSTRACT iv
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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[10]Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2004, pp. 28-31.
[11]S. Du, M. Shehata, and W. Badawy, "Hard hat detection in video sequences based on face features, motion and color information," in Computer Research and Development (ICCRD), 2011 3rd International Conference on, 2011, pp. 25-29.
[12]R. Waranusast, N. Bundon, V. Timtong, C. Tangnoi, and P. Pattanathaburt, "Machine vision techniques for motorcycle safety helmet detection," in IVCNZ, 2013, pp. 35-40.
[13]T. Cover and P. Hart, "Nearest neighbor pattern classification," Information Theory, IEEE Transactions on, vol. 13, pp. 21-27, 1967.
[14]M.-W. Park and I. Brilakis, "Construction worker detection in video frames for initializing vision trackers," Automation in Construction, vol. 28, pp. 15-25, 2012.
[15]S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, "Tracking groups of people," Computer Vision and Image Understanding, vol. 80, pp. 42-56, 2000.
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[17]楊佳穎, "以膚色資訊加速之 AdaBoost 即時人臉偵測系統," 臺灣大學工程科學及海洋工程學研究所學位論文, pp. 1-64, 2011.
[18]A. R. Smith, "Color gamut transform pairs," in ACM SIGGRAPH Computer Graphics, 1978, pp. 12-19.
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[22]支持向量機, http://zh.wikipedia.org/wiki/%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA.


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