基於行車紀錄器之日間煞車燈偵測系統

Vision-Based Daytime Brake Light Detection Using Front-Mounted Car Camcorder

吳羿蒨(Yi-Chien Wu)

指導教授 ： 李素瑛 ； 陳華總

國立交通大學/資訊學院/資訊科學與工程研究所/碩士(2015年)

https://doi.org/10.6842/NCTU.2015.00100

摘要

由於汽車的數量大增，行車安全議題在近幾年備受重視，加速了駕駛安全輔助系統的發展，駕駛安全輔助系統透過持續監控車輛周圍的環境及駕駛人的行為以提前偵測潛在的危機。在用路安全方面，煞車燈的偵測對於預防駕駛發生致命意外或造成車輛受損嚴重的碰撞是非常重要的一環，由於日間的光線變化甚大，在日間偵測煞車燈成為備受挑戰的議題。
本論文提出了利用行車紀錄器之視訊處理日間煞車燈的偵測系統。一開始我們先偵測車輛，接著我們利用多數煞車燈為紅色的特性來擷取出車尾燈，並利用車燈對稱的特性來定位車燈的位置以及驗證車輛偵測的正確性。接著，我們利用車燈的亮度搭配放射狀對稱偵測達到判斷煞車燈狀態的目的。我們使用行車記錄器錄製的影片來做實驗，實驗結果說明了我們提出的煞車燈偵測方法能夠正確判斷煞車燈狀態。

關鍵字

自動車 ； 駕駛安全輔助系統 ； 車輛偵測 ； 車尾燈偵測 ； 煞車燈偵測

並列摘要

Due to the rapid expansion of car ownership worldwide, driving traffic safety becomes a recently rising issue in the automobile industry. Increasing driver assistance systems have been developed to detect potential problems in advance by continuously monitoring the vehicle surroundings and the driving behaviors. Detecting brake lights during the daytime is a challenging work due to varied illumination conditions and is of vital importance for preventing drivers from mortal and costly rear-end collisions.
In this thesis, we propose a vision-based daytime brake light detection system using a front-mounted car camcorder, which tends to be widespread deployed. First, front vehicle candidates are detected by a cascade of Gentle Adaboost classifiers utilizing Histogram of Oriented Gradients descriptors. To improve the detection accuracy, symmetry of taillights is further employed to filter out the false detected candidates. Once a vehicle is detected, the regions of its taillights are utilized to differentiate the brake lights from nonbrake lights by investigating both radial symmetry and luminance features. Experiments conducted on real videos captured by front-mounted car camcorders demonstrate that satisfactory results can be obtained by the proposed daytime brake light detection system.

並列關鍵字

Automobile ； driver assistance system ； vehicle detection ； taillight detection ； brake light detection

參考文獻

[2] H. T. Chen, L. W. Tsai, H. Z. Gu, S. Y. Lee, and B. S. P. Lin, “Traffic congestion classification for nighttime surveillance videos,” Proceedings of IEEE International Conference on Multimedia and Expo Workshops, pp. 169-174, 2012.

[3] M. S. Shehata, J. Cai, W. M. Badawy, T. W. Burr, M. S. Pervez, R. J. Johannesson, and A. Radmanesh, “Video-based automatic incident detection for smart roads: the outdoor environmental challenges regarding false alarms,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 2, pp. 349-360, 2008.

[4] J. C. McCall and M. M. Trivedi, “Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 20-37, 2006.

[6] M. M. Trivedi, T. Gandhi, and J. McCall, “Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 1, pp. 108-120, 2007.

[7] W. Liu, X. Wen, B. Duan, H. Yuan and N. Wang, “Rear vehicle detection and yracking for lane change assist,” IEEE Intelligent Vehicles Symposium, pp. 252-257, 2007.