臺灣博碩士論文加值系統

自從菸害防制法實施以來，台灣對於在公共場所吸菸的限制越趨嚴格，其重視程度可由近兩年頻繁修法之動作看出。為了能有效地提高防制的成效以及降低人員監視成本，本論文提出一個植基人臉偵測下之抽菸行為識別系統，此系統能快速且準確地偵測出該影像中之人物是否有吸菸行為。首先，系統得利用現有臉部偵測方法定位出嘴巴位置，再將偵測出的臉部影像進行白平衡處理，以降低因光線而產生之誤判情況，接著套用HSV模式進行色彩分割，以及雜訊去除動作，決定出香菸之位置，根據此位置與口範圍來判別是否有人吸菸。本研究希望配合室內監視系統，能自動地偵測違規行為並發生出警告通知至負責單位作為參考。

Since the implementation of Tobacco Hazards Prevention Act, the prohibition against smoking in public places tends to be stricter in Taiwan. Its emphasis level can be inferred from the frequent law amendments in recent years. In order to effectively improve the prevention effect and bring down the costs of human surveillance, this paper proposes a smoking behavior detection system based on the human face analysis, which can quickly and accurately detect whether the person in the image is smoking or not. First, the system makes use of the existing facial detection scheme to locate the mouth, and then conducts white balance processing on the detected face image so as to reduce the misjudgment caused by the light. It then implements color segmentation and noise elimination in HSV color system in order to determine the location of the cigarette. The range between the location and the mouth is used to determine whether the person is smoking or not. This study hopes to combine with the indoor monitoring system to automatically detect the illegal behavior and send out alarms to notify the responsible units for reference.

摘要
Abstract
致謝
目錄
圖目錄
表目錄

第一章、緒論
1.1 研究動機與目的
1.2 文獻回顧
1.3 研究貢獻
1.4 論文架構
第二章、背景知識
2.1 色彩系統 (Color System)
2.1.1 YCbCr彩色系統
2.1.2 HSV彩色系統
2.2 人臉偵測
2.3 嘴部定位
2.4 二值型態學
2.5 白平衡
2.5.1 灰色世界假設法 (Gray World Assumption)
2.5.2 完美反射物假設法 (Perfect Reflector Assumption)
2.5.3 Lam Algorithms
第三章、研究方法與流程
第四章、實驗結果與討論
第五章、結論
參考文獻

[1]Chi-Shih Chao, Wen-Hua Chang, and Chuang-Chien Chiu, “A tele-medical monitoring system for human falls,” Proceedings of the 2005 Symposium on Digital Life and Internet Technologies, Taiwan, 2005, pp. 69-75.
[2]Rafael C. Gonzalez and Richard E. Woods, “Digital image processing,” Second Edition, Prentice Hall, 2002.
[3]Rein-Lien Hsu, Mohamed Abdel-Mottaleb, and Anil K. Jain, “Face detection in color images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, 2002, pp. 696-706.
[4]Sunita Kumari, Pankaj Kumar Sa, and Banshidhar Majhi, “Gender classification by principal component analysis and support vector machine,” Proceedings of the 2011 International Conference on Communication, Computing & Security, Odisha India, 2011, pp. 339-342.
[5]Edmund Y. Lam, “Combining gray world and retinex theory for automatic white balance in digital photography,” Proceedings of the Ninth International Symposium on Consumer Electronics, Hong Kong, 2005, pp.134 -139.
[6]Hsun-Chun Lee and Daw-Tung Lin, “Intelligent surveillance system for halt detection and people counting,” Journal of Information Technology and Applications, Vol. 2, No. 3, 2007, pp. 141- 150.
[7]Chia-Jung Pai, “Pedestrian detection and tracking at crossing,” Master Thesis, National Taiwan Normal University, Taiwan, 2002.
[8]Chih-Hao Su, “Surveillance of clinical ward activities using computer vision,” Master Thesis, Asia University, Taiwan, 2006.
[9]Bijan Shoushtarian and Helmut E. Bez, “A practical adaptive approach for dynamic background subtraction using an invariant colour model and object tracking,” Pattern Recognition Letters, Vol. 26, No. 1, 2005, pp. 5-26.
[10] Chi-Tsan Wang, “A real-time monitoring system based on video and voice
technique,” Master Thesis, Kun Shan University, Taiwan, 2008.
[11] Fang-Ming Yu, Han-Ding Li, Yao-Wei Chiu, Chang-Herng Yih, and Huang-An
Yang, “Using image identification aids for medical service nursing,” Proceedings of the 2009 Symposium on Application and Development of Management Information System, Taiwan, 2009, pp. 146-150.