臺灣博碩士論文加值系統

視覺式駕駛安全輔助系統相關技術在距今約二十多年前開始被重視與開發，透過攝影機以視覺式的方式分析車輛前方道路的狀況來輔助駕駛者。其中針對汽車之視覺式駕駛安全輔助系統近年來已逐漸完善，反觀機車之視覺式駕駛安全輔助系統並未被重視。機車以及汽車數量逐年提高，而每年機車上升的數量較汽車多了約五萬。上述情況最終導致汽車交通肇事率逐年降低，而機車交通肇事率逐年上升的問題。
前方車輛偵測技術於白天場景已逐漸成熟，但是較少研究者針對夜晚場景進行開發與研究。透過近年來夜晚前方車輛偵測技術文獻可知，許多研究會藉由車尾燈偵測之相關技術，確認車輛位置。因此本研究將利用車尾燈偵測確認車輛位置，而由於本研究需進行前方車輛減速偵測，故本研究將針對車尾燈區域之剎車燈啟動與否判斷前方車輛是否減速。
由於機車有轉彎的情況，因此本研究將進行Region of Interest (ROI) 範圍調整。當車輛遇紅色交通號誌停止移動時，因不會與前方車輛發生交通事故，所以不需進行車尾燈偵測以及剎車燈啟動偵測，故本研究需偵測前方車輛是否移動。由於近年來車輛之車尾燈並不一定為傳統圓形形狀之車尾燈，還有不規則形狀之車尾燈以及長條形狀之車尾燈，因此本研究將針對車尾燈周圍環繞光源的特性進行車尾燈偵測。本研究於剎車燈啟動偵測中將利用其亮度以及門檻值判斷是否啟動，而此門檻值為動態形式，將根據車尾燈至攝影機之距離的不同決定其門檻值。由於某些剎車燈啟動時其亮度值低於本研究決定之門檻值以及某些剎車燈未啟動時其亮度值高於本研究決定之門檻值，將導致剎車燈啟動偵測失敗。因此本研究將針對此類車尾燈個別調整其門檻值，以提高剎車燈啟動偵測之正確率。
最後實驗的部分，本研究針對晴天、雨天以及隧道三種場景以及數種道路種類進行實驗。實驗結果呈現出，本研究在不考慮地面紅色反光時車尾燈偵測結果皆能產生較高的正確率，而地面紅色反光期望於未來能進行過濾，以提升車尾燈偵測正確率。本研究於剎車燈啟動偵測中若不考慮雨天時因雨滴滴落鏡頭上的情況，則剎車燈啟動偵測之正確率約略為90%。

Vision-based driver assistance systems and its related technologies were started to pay attention and develop from about 20 years ago. Visual analyzing the road situation in front of vehicles through camera to assist drivers. Vision-based driver assistance systems for automobile has been gradually consummated. In contrast, vision-based driver assistance systems for motorcycle went unheeded. The quantity of motorcycle and automobile increases year by year, and the quantity of motorcycle is fifty thousand more than automobile per year. Summarizes the above situation causes that automobile traffic accident rate reduces year after year, but motorcycle traffic accident rate rises every year.
Daytime forward vehicle detection technology has been matured by degrees, but there is not so much researchers developing and researching at nighttime. By literatures in recent years of nighttime forward vehicle detection technology, many researches confirm the location of vehicle through related technologies about taillight detection. Therefore this study will use taillight detection to confirm the location of vehicle. Because it has to do a forward vehicle deceleration detection, forward vehicle decelerates or not will be determined by the brake-lights activates or not.
When the motorcycle turns a corner, this study will adjust Region of Interest (ROI). There will not be traffic accidents with the forward vehicle when the vehicle stop moving as the red traffic light shows. So it hasn’t to do a taillight detection and brake-light detection. Therefore our system needs to detect forward vehicle move or not. The shape of taillight in the recent years is not only traditional circle but also irregular shape or elongated shape, and therefore this study will aim at the characteristic of surrounding light source around the taillight to do a taillight detection.
This study will use illumination and threshold to determine brake-light on or off, and this dynamic threshold according to the distance between taillight and camera. The illumination of some activated brake-lights is lower than our determined threshold, and some non-brake of taillights are higher than it. It will lead to failure of brake-light detection. So our system will adjust threshold specifically to increase the accuracy rate of brake-light detection.
Our system experiments on sunny day, rainy day, in the tunnel, and on many kinds of roads. The experiment result shows that it will get the higher accuracy rate without considering the consequence of taillight detection with the reflection of red lights. And our system expects that the reflection of red lights can be filter in the future to increase the accuracy rate of taillight detection. In this study, if it doesn’t consider raindrop dripping on the camera lens on rainy day, the accuracy rate of brake-light detection is about 90%

摘要 I
Abstract II
誌謝 IV
目錄 V
圖目錄 VII
表目錄 X
第一章 緒論 1
第一節 研究動機 1
第二節 研究困難 5
第三節 論文架構 6
第二章 文獻探討 7
第一節 白天以及夜晚前方車輛偵測系統技術分析 7
第二節 剎車燈啟動偵測 14
第三章 夜晚機車前方車輛減速偵測系統 22
第一節 系統目的 22
第二節 研究環境與設備 22
第三節 系統流程 23
第四章 ROI範圍調整及前方車輛移動偵測 25
第一節 ROI範圍調整 25
第二節 前方車輛移動偵測 31
第五章 車尾燈追蹤與剎車燈啟動偵測 35
第一節 車尾燈偵測 35
第二節 車尾燈追蹤 41
第三節 剎車燈啟動偵測 44
第四節 方法改良 46
第六章 實驗結果 50
第一節 晴天 51
第二節 雨天 58
第三節 隧道 67
第七章 結論與未來工作 70
第一節 結論 70
第二節 未來工作 70
參考文獻 72

M. I. Arenado, J. M. P. Oria, C. Torre-Ferrero, L. A. Rentería, “Monovision-based Vehicle Detection, Distance and Relative Speed Measurement in Urban Traffic,” IET Intelligent Transport Systems, vol. 8, no. 8, pp 655-664, 2014.
V. D. Nguyen, T. T. Nguyen, D. D. Nguyen, S. J. Lee, and J. W. Jeon, “A Fast Evolutionary Algorithm for Real-Time Vehicle Detection,” IEEE Transactions on Vehicular Technology, vol. 62, no. 6,pp 2453-2467, 2013.
X. Wen, L. Shao, W. Fang, and Y. Xue, “Efficient Feature Selection and Classification for Vehicle Detection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 3, pp 508-517, 2015.
R. O’Malley, E. Jones, and M. Glavin, “Rear-Lamp Vehicle Detection and Tracking in Low-Exposure Color Video for Night Conditions,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp 453-462, 2010.
N. Kosaka and G. Ohashi, “Vision-Based Nighttime Vehicle Detection Using CenSurE and SVM,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp 2599-2608, 2015.
M. Rezaei, M. Terauchi and R. Klette, “Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp 2723-2743, 2015.
D. Y. Chen and Y. J. Peng, “Frequency-Tuned Taillight-Based Nighttime Vehicle Braking Warning System,” IEEE Sensors Journal, vol. 12, no. 11, pp 3285-3292, 2012.
D. Y. Chen, Y. H. Lin, and Y. J. Peng, “Nighttime Brake-Light Detection by Nakagami Imaging,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 4, pp 1627-1637, 2012.
A. Almagambetov, S. Velipasalar, and M. Casares, “Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras,” IEEE Transactions on Industrial Electronics, vol. 62, no. 6, pp 3732-3741, 2015.
H. T. Chen, Y.C. Wu, and C.C. Hsu, “Daytime Preceding Vehicle Brake Light Detection Using Monocular Vision,” IEEE Sensors Journal, vol. 16, no. 1, pp 120-131, 2016.
G. T. Kaya, “A Hybrid Model for Classification of Remote Sensing Images With Linear SVM and Support Vector Selection and Adaptation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 4, pp 1988-1997, 2013.
D. Duan, M. Xie, Q. Mo, Z. Han and Y. Wan, “An Improved Hough Transform for Line Detection,” IEEE International Conference on Computer Application and System Modeling, Beijing, China, pp 354-357, 2010.
B. J. Ji, L. S. Lan, “Optical Flow Assisted Video Object Detection in Dynamic Background,” National Yunlin University of Science and Technology, 2012.
K. R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, “An Introduction to Kernel-Based Learning Algorithms,” IEEE Transactions on Neural Networks, vol. 12, no. 2,pp. 181-201, 2001.
C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining Knowl. Discovery, vol. 2, no. 2, pp. 955–974, 1998.
R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Trans. ASME, J. Basic Eng., vol. 82, pp. 35–45, 1960.
D. Y. Chen, Y. J. Peng, L. C. Chen, and J. W. Hsieh. “Nighttime Turn Signal Detection by Scatter Modeling and Reflectance-Based Direction Recognition,” IEEE Sensors Journal, vol. 14, no. 7, pp 2317-2326, 2014.
全國法規資料庫，道路交通安全規則，取自http://la w.moj.gov.tw/Law/LawSearchResult.aspx?p=A&t=A1A2E1F1&k1=道路交通安全規則，2015年。
機車分類，取自https://zh.wikipedia.org/wiki/%E8%87%BA%E7% 81%A3%E6%A9%9F%E8%BB%8A，2015年。
警政統計年報，取自https://www.npa.gov.tw/NPAGip/wSite/ct?xItem= 26918&ctNode=12595&mp=1，2015年。
中華民國交通部總局，機車道路交通事故概況分析，取自htt p://www.hb.gov.tw/sites/ch/search?q=機車道路交通事故概況分析，2015年。
交通部統計查詢網，A1類道路交通事故－按車種分，取自http://stat.mot c.gov.tw/mocdb/stmain.jsp?sys=100，2015年。
中華民國內政部警政署，103年警察機關受(處)理A1類道路交通事故概況，取自http://www.npa.gov.tw/NPAGip/wSite/ct?xItem=74211&ctNo de=12594&mp=4，2014年。
中華民國內政部警政署，102年警察機關受(處)理A1類道路交通事故概況，取自http://www.npa.gov.tw/NPAGip/wSite/public/A ttachment/f139 5369239706.pdf，2013年。
果凍現象，取自http://www.flexmedia.com.tw/index.php/tutor ials/17-taiw an/learning/32-oscillation，2015年。
跟車剎車距離計算，取自http://0123456789.tw/?p=2448，2015年。
GoPro Hero4 Silver Edition規格，取自http://www.mgrstore.net/detail. php?rid=510，2015年。
Harris及Shi-Tomasi原理，取自http://blog.csdn.net/luoshixian099/article/ details/48244255，2015年
Lucas-Kanade光流法，取自https://zh.wikipedia.org/wiki/%E5%8D%A2%E5%8D%A1%E6%9 6%AF-%E5%8D%A1%E7%BA%B3%E5%BE%B7%E6%96%B9%E6%B3%95，2015年