Research of Fog Driving Scenarios and Visibility Recognition Algorithm Based on Video
Abstract: Obtaining real-time, comprehensive and accurate road traffic information is the important pre-condition and basic guarantee to prevent traffic accidents, and also is the key to realize the urban traffic intelligent. For recognition of fog driving scenarios and visibility, the traditional algorithm has the problems of high complexity, poor robustness, and more using in fixed scene; it is difficult to apply to mobile driving scenarios. This paper proposed a fog and visibility estimation algorithm based on monocular vision. The algorithm, based on the law of Koschmieder, compresses Hough transformation vote space and reduces calculation amount and complexity by limiting polar angle and radius. Custom regional growth solves the problem of poor accuracy in the mobile scenarios’ road segmentation. The weighted average of luminance method which is used in estimation of inflection point can effectively remove interference and ensure accuracy. The simulation results show that the algorithm can realize the recognition of fog and visibility in mobile scenarios with high accuracy, real-time performance and robustness.
文章引用: 朱舞雪 , 宋春林 (2015) 基于视频的雾天驾驶场景及其能见度识别算法研究。 图像与信号处理， 4， 67-77. doi: 10.12677/JISP.2015.43008
Bronte, S., Bergasa, L.M. and Alcantarilla, P.F. (2009) Fog detection system based on computer vision techniques. International IEEE Conference on Intelligent Transportation Systems, St. Louis, 4-7 October 2009, 1-6.
Hautiere, N., Labayrade, R. and Aubert, D. (2006) Real-time disparity contrast combination for onboard estimation of the visibility distance. IEEE Transactions on Intelligent Transportation Systems, 7, 201-212.
 宋洪军, 陈阳舟, 郜园园 (2013) 基于交通视频的雾天检测与去雾方法研究. 控制工程, 6, 1156-1160.
 宋晓建, 杨玲(2011) 基于图像退化模型的天气现象识别. 成都信息工程学院学报, 2, 132-136.
 李骞, 范茵, 张璟, 李宝强 (2011) 基于室外图像的天气现象识别方法. 计算机应用, 6, 1624-1627.
Negru, M. and Nedevschi, S. (2013) Image based fog detection and visibility estimation for driving assistance systems. IEEE International Conference on Intelligent Computer Communication & Processing, 5-7 September 2013, 163-168.
Hautiére, N., et al. (2006) Automatic fog detection and estimation of visibility distance through use of an onboard camera. Machine Vision & Applications, 17, 8-20.
Canny, J. (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysis & Machine Intelligence, PAMI-8, 184-203.
 袁萍 (2014) 高速公路恶劣天气及交通状况智能分析系统研究与实现. 硕士论文, 西南交通大学, 成都.
Salvucci, D.D. (2004) Inferring driver intent: A case study in lane-change detection. Human Factors & Ergonomics Society Annual Meeting Proceedings, 48, 2228-2231.