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姓名 蔡文耀(Wen-yao Tsai) 查詢紙本館藏 畢業系所 資訊工程學系 論文名稱 使用熱像儀做夜間跌倒偵測
(Falling Down Detection At Night By Thermal Imager)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 跌倒是發生危險的重要依據，不管是因為自己行走不慎發生的跌倒，還是身體不適導致的跌倒，如果沒有及時發現很可能會錯失搶救的黃金時間。
本論文欲實現在深夜光線不足環境下的安全監控，可能是室內的老人看護或是夜間的斑馬線上。實驗著重於行人跌倒的部分，若能藉由本系統即時偵測出是否有人跌倒不起，可適時增加紅綠燈的秒數，甚至是啟動警報系統通知保全或是鄰近醫院給予救援，如此一來將可大大提高搶救的成功率。
有別於一般安全監控的系統，在夜間環境下會因光線不足而增加大量雜訊，因此無法使用單純的監視錄影機去實現本系統。市面上之紅外線監視器是用紅外線輔助光源增加夜間影像擷取強度，但若在完全漆黑的環境下，取得的影像依然不夠清晰，增加影像處理之困難。 若使用熱像儀作為監視器，即使在沒有任何光源的環境下，也可以擷取出完整的人體輪廓，且可依溫度高低輕易地去除背景，利於實踐人體相關之影像處理。經實驗結果證明我們的系統在夜間斑馬線上是可行且具有良好偵測率。
摘要(英) Falling down is an important factor for dangerous evaluation. No matter it is caused due to physical discomfort or accident. If it is not detected at real time, it will probably miss the prime time of rescuing.
In this thesis, we focus on safety monitoring in the night of low-light environments. The situations occur frequently for indoor elderly care at night or pedestrian zebra crossing at night. Our work is designed mainly on falling down detection of pedestrians. If the system detects the situation that someone falls down in zebra crossing and can not climb up in a certain period of time, it will be synchronized with the traffic light controller to increase the green light period on the zebra crossing side. In the meantime, an alert is triggered. Alert is issued in the form of sound or flashing alarm whenever falling down situation is detected to warn the drivers.
Different from general traffic safety system capturing images by CCD cameras, noises and environmental effects usually generate images with very poor quality and lose a lot of originally visible information at night. Hence, traditional camera-based systems do not work well. To alleviate this problem, some security systems add infrared ray to assist image capturing. However, the images are still not clear enough to assure the normal operation of most security systems in the fully dark environment. In our work, thermal cameras are adopted instead to replace traditional cameras to overcome the aforementioned problems. Thermal cameras can extract clear and complete body contours even in the absence of lights. Experimental results demonstrate the feasibility and validity of our proposed method in detecting the occurrence of falling down, especially in nighttime.
關鍵字(中) ★ 熱像儀
★ 跌倒
★ 光流關鍵字(英) ★ thermal imager
★ fall
★ optical flow論文目次 中文摘要…………………………………………………………………i
Abstract………………………………………………………………ii
致謝……………………………………………………………………vv
目錄…………………………………………………………………v
圖片目錄………………………………………………………………viii
表格目錄…………………………………………………………x
第一章 簡介 ……………………………………………………………1
1.1 系統簡介及實驗動機 ………………………………………………1
1.2 相關研究 …………………………………………………………2
1.3 實驗環境設定 ……………………………………………………4
1.4 系統流程……………………………………………………………6
第二章 影像主要區域及特偵值擷取 ………………………………9
2.1 影像主要執行區域之定義 ………………………………………9
2.2 行人分割 …………………………………………………………11
2.2.1 Otsu二值化演算法簡介 …………………………………11
2.2.2 連通單元演算法簡介 ……………………………………12
2.2.3 形態學影像處理介紹 ……………………………………14
2.2.4 實作行人分割及問題 ……………………………………15
2.3 光流演算法 ………………………………………………………17
2.3.1 光流演算法介紹 …………………………………………17
2.3.2 多張影像之光流計算 ……………………………………18
2.3.3 多張光流計算之權重值 …………………………………23
2.3.4 平均垂直光流向量 ………………………………………25
第三章 跌倒偵測系統 ………………………………………………27
3.1 特徵值的選取及正規化 …………………………………………27
3.1.1 跌倒的時間窗口定義 ……………………………………27
3.1.2 運動歷史影像 ……………………………………………29
3.2 跌倒狀態的決策 …………………………………………………33
3.2.1單純貝氏分類器的簡介 ……………………………………35
3.2.2 單純貝氏分類器的訓練與分類 …………………………37
3.3 跌倒偵測系統上的重疊問題 ……………………………………37
3.3.1 跌倒重疊的問題與困難 …………………………………37
3.3.2 解決重疊的替代方案 ……………………………………39
3.4 跌倒偵測的警報系統 ……………………………………………41
第四章 實驗結果 ……………………………………………………43
4.1 一般情況的斑馬線上跌倒偵測 …………………………………43
4.1.1 行走部分的辨識率 ………………………………………43
4.1.2 跌倒部分的辨識率 ………………………………………45
4.2 特殊狀況的斑馬線上跌倒偵測 …………………………………46
4.2.1 蹲下狀況的結果 …………………………………………47
4.2.2 跌倒重疊的結果與議題 …………………………………48
4.3 跌倒偵測系統的最後辨識率 ……………………………………49
第五章 總結 …………………………………………………………51
5.1 結論 ………………………………………………………………51
5.2 未來工作 …………………………………………………………52
參考文獻 ………………………………………………………………54
參考文獻 [1] Zaw Zaw Htike, Simon Egerton, Kuang Ye Chow, A Monocular View-invariant Fall Detection System for the Elderly in Assisted Home Environments , Seventh International Conference on Intelligent Environments ,2011
[2] Lina Tong, Wei Chen, Quanjun Song, Yunjian Ge , A Research on Automatic Human Fall Detection Method Based on Wearable Inertial Force Information Acquisition System , International Conference on Robotics and Biomimetics , December 2009
[3] Qiang Li, John A. Stankovic, Mark A. Hanson, Adam T. Barth, John Lach , Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , IEEE - Body Sensor Networks 2009
[4] Dima Litvak, Student Member , Fall Detection of Elderly through Floor Vibrations and Sound , IEEE EMBS August 2008
[5] B. Najafi, K. Aminian, Falling Risk Evaluation in Elderly Using Miniature , IEEE EMBS October 2000
[6] Shunichi Aoyagi , Yuichi Chida , On-Line Distinction Methods of Human Falling Motions Based on Machine Learning , SICE Annual Conference August 2010
[7] Chin-Feng Lai and Yueh-Min Huang , Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors , IEEE Computer Society 2010
[8] Chia-Wen Lin , Zhi-Hong Ling , Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare , IEEE ,2007.
[9] Huimin Qian, Yaobin Mao, Wenbo Xiang, and Zhiquan Wang , Home Environment Fall Detection System Based On a Cascaded Multi-SVM Classifier , Control, Automation, Robotics and Vision Hanoi, Vietnam, 17–20 December 2008
[10] Bin Huang, Guohui TIAN and Xiaolei LI , A Method for Fast Fall Detection , Intelligent Control and Automation ,June 2008
[11] Young-Sook Lee and HoonJae Lee , Multiple Object Tracking for Fall Detection in Real-Time Surveillance System , Feb. 15-18,ICACT 2009
[12] Yie-Tarng Chen, Yu-Ching Lin, Wen-Hsien Fang, A Hybrid Human Fall Detection Scheme, IEEE 17th International Conference on Image Processing , September 2010
[13] V.Vaidehi, Kirupa Ganapathy, K.Mohan, A.Aldrin, K.Nirmal , Video Based Automatic Fall Detection In Indoor Environment , International Conference on Recent Trends in Information Technology, ICRTIT 2011
[14] Qingcong , A Poselet-based Approach for Fall Detection , IEEE ,2011
[15] Homa Foroughi, Alireza Rezvanian, Amirhossien Paziraee , Robust Fall Detection using Human Shape and Multi-class Support Vector Machine , IEEE ICVGIP ,2008
[16] NOBUYUKI OTSU , A Tlreshold Selection Method from Gray-Level Histograms , IEEE Transactions on systems, man, and cybernetics, VOL. SMC-9, NO. 1, January 1979
[17] B. K. P. Horn and B. G. Schunck, “Determining Optical Flow,” Artificial Intelligence,Vol. 17, 1981, pp. 185-203.
[18] Chia-Ming Wang, Kuo-Chin Fan and Cheng-Tzu Wang, Estimating Optical Flow by Integrating Multi-Frame Information, Journal of Information Science and Engineering 24, 1719-1731 (2008)
[19] James W. Davis and Aaron F. Bobick , The Representation and Recognition of Action Using Temporal Templates, CVPR 2007
指導教授 范國清(Kuo-chin Fan) 審核日期 2012-6-19 推文 plurk
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