行車安全偵測系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：50

、訪客IP：3.145.79.65

姓名

熊昭岳(Chao-Yueh Hsiung) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

行車安全偵測系統
(An Attention Detection System for Vehicles)

相關論文

★ 以Q-學習法為基礎之群體智慧演算法及其應用	★ 發展遲緩兒童之復健系統研製
★ 從認知風格角度比較教師評量與同儕互評之差異：從英語寫作到遊戲製作	★ 基於檢驗數值的糖尿病腎病變預測模型
★ 模糊類神經網路為架構之遙測影像分類器設計	★ 複合式群聚演算法
★ 身心障礙者輔具之研製	★ 指紋分類器之研究
★ 背光影像補償及色彩減量之研究	★ 類神經網路於營利事業所得稅選案之應用
★ 一個新的線上學習系統及其於稅務選案上之應用	★ 人眼追蹤系統及其於人機介面之應用
★ 結合群體智慧與自我組織映射圖的資料視覺化研究	★ 追瞳系統之研發於身障者之人機介面應用
★ 以類免疫系統為基礎之線上學習類神經模糊系統及其應用	★ 基因演算法於語音聲紋解攪拌之應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

目前應用到車上的安全設備，大多都是在事故發生時，去保護駕駛人的生命安全，例如安全帶、安全氣囊等，如此消極的作法無法減少事故的發生。目前，已有許多研究者設計出各種車用安全系統，主要包含駕駛人疲勞偵測、行車狀態分析等，來偵測駕駛者安全與否。
基於上述論點，本論文提出一套行車安全偵測系統，此系統能偵測出駕駛人的行為及行車狀態，並能在事故發生前，有效的發出警告，如此一來，便可大幅地降低事故的發生。
本系統包含駕駛人注意力偵測及行車狀態分析兩個部份。在駕駛人注意力偵測部份，利用影像處理的技術，來找出眼睛及臉部中心的位置，由這三個特徵點所連結的三角形中三個邊的向量、三個夾角的資訊，去估算出駕駛者臉朝的方向。藉由人臉的偵測、三點特徵及人臉的朝向，我們可以定出一些規則來偵測駕駛人的注意力，例如眼睛偵測不到、眨眼頻率過高及頻繁地偏向別處等狀態。在行車狀態分析的部份，則是利用一個加速度感測器，此裝置可以同時測量兩個不同方向的加速度。將感測器架設在車上，記錄其因搖擺而輸出的數據，接著，我們使用有限狀態機去分析目前的行車狀態，在發覺行車狀態中有不當的駕駛行為時，有效的發出警告。它不僅僅可用於即時的偵測與警示，也可作為事後的推斷及分析，如此一來，我們可以在事後去觀察駕駛人不當的駕駛行為。最後，我們藉由實驗結果，證明了此系統在行車安全上之可行性及效能。

摘要(英)

At present, there are many commercially available safety equipments such as the safety belts, airbags, etc, on vehicles. They are used to diminish the degree of injury while car accidents happen. These passive safety equipments can’’t really prevent the occurring of car accidents in practice. Recently, many various safety systems have been proposed. These safety systems concentrate on the driver fatigue detection, lane detection, etc.
In this thesis, an attention detection system for vehicles is proposed to detect the driver’s driving behaviors and driving states while driving. Via the attention detection system warning signals will be issued in time when an accident is going to happen. In this way, car accidents can then be greatly reduced.
There are two functional blocks in the attention detection system. They include the driver attention detection block and the driving state analysis block. In the driver attention detection block, a web camera is used to acquire driver’s images while driving. Some image processing techniques are utilized to locate the positions of eyes and the center of the face. These three feature points consist of a triangle. The three edge vectors and the three included angles in the triangle can then be used to estimate the driver’s face orientations. In addition to the information about the face orientations, information about the eye-closure frequency and the information about the appearance of the face are integrated to determine whether the driver’s attention degree. The driving state analysis block involves in the use of an accelerometer circuit. The accelerometer can measure the degree of the acceleration of two different directions at the same time. The accelerometer circuit will record the driving states of the car. Then a finite state machine is used to analyze the driving states. Signals can be issued in time to warn the driver while improper driving behaviors are detected. In fact, we can also use the driving state analysis block to determine how often a bus or truck driver exhibits improper driving behaviors after he or she completes a journey. Experiments were conducted to test the performance of the proposed attention detection system.

論文目次

摘要....................................I
Abstract..............................III
誌謝....................................V
目錄...................................VI
表目錄...............................VIII
圖目錄.................................IX
第一章緒論.........................1
1.1 研究動機 ......................1
1.2 文獻回顧......................3
1.2.1 疲勞度偵測....................3
1.2.2 行車狀態分析..................5
1.3 研究目的 ......................6
1.4 論文架構 ......................6
第二章相關技術之介紹與探討.........7
2.1 影像處理相關技術..............7
2.1.1 色彩分割 ......................7
2.1.2 灰階轉換 .....................10
2.1.3 二值化處理...................10
2.2 加速度感測器之介紹...........11
2.2.1 加速度感測器基本原理.........11
2.2.2 傾斜度感測原理...............14
第三章研究方法與步驟..............16
3.1 駕駛人注意力偵測.............16
3.1.1 臉部偵測 .....................18
3.1.2 眼睛偵測 .....................21
3.1.3 方向偵測 .....................25
3.1.4 注意力偵測...................29
3.2 行車狀態分析.................31
3.2.1 資料平滑化...................32
3.2.2 線性迴歸斜率計算.............33
3.2.3 加速度感測器輸出模糊化.......34
3.2.4 以有限狀態機分析行車狀態.....39
3.2.5 離線分析 .....................44
第四章系統介面介紹與實驗結果......46
4.1 注意力偵測系統介紹...........46
4.1.1 系統設備 .....................46
4.1.2 系統介面 .....................47
4.2 行車狀態分析系統介紹.........48
4.2.1 系統設備 .....................48
4.2.2 加速度感測平台介紹...........49
4.2.3 微處理器之簡介...............50
4.2.4 系統介面 .....................51
4.3 實驗結果 .....................53
4.3.1 注意力偵測系統之實驗結果.....53
4.3.2 行車狀態分析系統之實驗結果...56
第五章結論與未來展望..............59
參考文獻...............................61
附錄A　加速度感測平台電路圖............65
附錄B　微處理器控制程式................66

參考文獻

[1] 王文俊，“認識Fuzzy”，全華科技圖書股份有限公司，2002。
[2] 林宸生，“數位信號影像與語音處理”，全華科技圖書股份有限公司，1998。
[3] 孫宗瀛，“FUZZY控制：理論實作與應用”，全華科技圖書股份有限公司， 1999。
[4] 陸蒔楠，“車用智慧型安全預警系統與行車資訊分析”，淡江大學電機工程學系碩士論文，2005。
[5] 鍾國亮，“影像處理與電腦視覺”，東華書局，2002。
[6] 蘇木春、張孝德，“機器學習：類神經網路、模糊系統以及基因演算法則”，全華科技圖書股份有限公司，2003。
[7] Atmel, Inc. AT89C51 8-bit MicroController with 4Kbytes Flash. AT89C51 Datasheet. Available:
http://www.atmel.com/dyn/resources/prod_documents/doc0265.pdf
[8] G. R. Bradski, “Computer Vision Face Tracking For Use in a Perceptual User Interface,” in Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 214-129, Oct. 1998.
[9] C. Y. Fang, S. W. Chen, and C. S. Fuh, “Automatic Change Detection of Driving Environments in a Vision-Based Driver Assistance System,” IEEE Trans. on Neural Networks, vol. 14, no. 3, pp. 646-657, May 2003.
[10] H. Frank, K. Frank, K. Rudolf, and R. Thomas, FUZZY CLUSTER ANALYSIS, John Wiley & Sons.
[11] R. S. Feris, T. E. Campos, and R. M. Cesar, “Detection and Tracking of Facial Features in Video Sequences,” in Mexican International Conference on Artifical Intelligence, pp. 129-137, 2000.
[12] Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learing and an application to boosting,” Journal of Computer and System Sciences, vol. 55, pp. 119-139, 1997.
[13] C. Garcia, G. Zikos, and G. Tziritas, “Face Detection in Color Images using Wavelet Packet Analysis,” in Proceedings of the 6th IEEE International Conference on Multimedia Computing and Systems, Florence, pp. 703-708, 1999.
[14] R. C. Gonazlez and R. E. woods, Digital image processing, Addison- wesley, 1992.
[15] W. B. Horng, C. Y. Chen, Y. Chang, and C. H. Fan, “Driver Fatigue Detection Based on Eye Tracking and Dynamic Template Matching,” in Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, vol. 1, pp.7-12, 21-23 March 2004.
[16] R. L. Hsu, M. A. Nottaleb, and A. K. Jain, “Face Detection in Color Images,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696-706, May 2002.
[17] S. Jaya and S. Thambipillai, “A Hybrid Approach to Designing an Autonomous Driving Alert System using Geometrical Features and Gray Level Information of Face Images,” in IEEE Region 5, 2003 Annual Technical Conference, pp.33-38, 11 April 2003.
[18] Q. Ji, Z. Zhu, and P. Lan, “Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue,” IEEE Trans. on Vehicular Technology, vol. 53, no. 4, pp. 1052-1068, July 2004.
[19] R. Lienhart, A. Kuranov, and V. Pisarevsky, “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection,” Technical Report, MRL, Intel Labs, 2002.
[20] C. Lin and K. C. Fan, “Human Face Detection Using Geomeric Triangle Relationship,” in Proceedings of the IEEE 15th International Conference on Pattern Recognition, vol. 2, pp. 941-944, 2000.
[21] C. H. Morimoto and M. Flickner, “Real-Time Multiple Face Detection Using Active Illumination,” in Proceedings of the Fourth IEEE International Conference on Antomatic Face and Gesture Recognition, pp. 1-6, March 2000.
[22] MEMSIC, Inc. Ultra Low Noise, 1g Dual Axis Accelerometer with Analog Output, MXA2500U Datasheet. Available:
http://www.memsic.com/memsic/data/products/MXA2500UL/MXA2500U.pdf
[23] MEMSIC, Inc. Accelerometer Fundamentals. #AN-00MX-001 Application Note. Available:
http://www.memsic.com/memsic/pdfs/an-00mx-001.pdf
[24] MEMSIC, Inc. Thermal Accelerometers Frequency Compensation. #AN-00MX- 003 Application Note. Available:
http://www.memsic.com/memsic/pdfs/an-00mx-003.pdf
[25] MEMSIC, Inc. Inclination Sensing with Thermal Accelerometers. #AN- 00MX-007 Application Note.
http://www.memsic.com/memsic/pdfs/an-00mx-007.pdf
[26] Open Source Computer Vision Library (OpenCV). Available:
http://sourceforge.net/projects/opencvlibrary/
[27] C. A. Perez, A. Palma, C. A. Holzmann and C. Pena, “Face and Eye Tracking Algorithm based on Digital Image Processing,” in IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1178-1183, 2001.
[28] M. Rizon and J. L. Wu, “Automatic Eye Detection Using Intensity and Edge Information,” in Proceedings of TENCON 2000, vol. 2, pp. 24-27, Sept. 2000.
[29] K. Sobottka and I. Pitas, “Extraction of Facial Regions and Features using Color and Shape Information,” in Proceedings of the IEEE 13th International Conference on Pattern Recognition, vol. 3, pp. 421-425, 1996.
[30] M. C. Su, C. Y. Hsiung, and G. D. Chen, “A DSP-Based Attention Detection System,” presented at the Biomedica Engineering Society 2004 Annual Symposium, Tainan, Taiwan.
[31] P. Viola and M. Jones, “Robust Real-time Object Detection,” in Proceedings of IEEE Workshop on Statistical and Theories of Computer Vision, 2001.
[32] R. B. Wang, K. Y. Guo, S. M. Shi, and J. W. Chu, “A Monitoring Method of Driver Fatigue Behavior Based on Machine Vision,” in Proceedings of IEEE Intelligent Vehicles Symposium, pp.110-113, 9-11 June 2003.
[33] S. H. Yeh, Human Facial Animation based on Real Image Sequence, Master’s Thesis, Computer Science and Engineering, National Sun Yat- Sen University, 2000.

指導教授

蘇木春(Mu-Chun Su)

審核日期

2005-7-13

推文