Research On Key Technologies Of State Identification Method For Highway Congestion Event Detection At Night

The highway at night compared to daytime more prone to traffic incident,causing traffic congestion.With the popularization and application of video monitoring system in a wide range of highway,highway congestion event detection based on video has been developing rapidly.The current video discrimination algorithm based on the congestion state is only applicable to broad daylight scenes,at night,there are many disadvantages such as poor lighting conditions,relatively dark environment and invisible body,the existing congestion identification algorithm of based on video is not difficult to meet the demand of highway congestion detection of night scene.Therefore,it is of great theoretical and practical significance to make full use of the existing conditions of highway and to design and implement the congestion state estimation algorithm.In view of this particular scene at night highway,in the analysis of the problems and difficulties of congestion distinguishing based on video at night highway state,this paper focuses on the research of the extraction of vehicle lights at night,the acquisition of traffic parameters suitable for the night,and the establishment of the model of nighttime congestion,finally form a traffic congestion discriminant algorithm which based on video on highway at night.The main research contents include:(1)In terms of lamp extraction,firstly,for the problems of there are a lot of noise in video images at night,by using the method of image enhancement preprocessing operations,to reduce noise interference and enhance the contrast of light and background area.Then,for the problems of poor adaptability of the conventional vehicle lights segmentation at night scene,using maximum entropy method of double threshold based on genetic algorithm for lamp segmentation.Finally,for the problems of the interference of ground reflected light which after image segmentation,a method of reflection light elimination based on gradient information is proposed.The experimental results show that,this method can effectively improve the accuracy of vehicle light extraction,while laying the foundation for accurate access to traffic parameters.(2)In terms of nighttime traffic parameters acquisition,in the night scene of highway,because there is no light,the body of car is not visible as a result of general traffic parameters are difficult to obtain,utilize the structural features of highway,to obtain the road length and number information through extraction the road lane line,based on this,get to combine with light and road information to achieve an average traffic density and time occupancy ratio of the highway scenes at night.(3)In terms of nighttime congestion identification,based on the fuzzy relationship between traffic states,by analysis of the relationship between two traffic parameters which average traffic density and time occupancy ratio,use the FCM algorithm to judge the traffic congestion at night,and reduce the error rate by the method of voting mechanisms,improved the accuracy of congestion identification at nighttime.Finally,according to the above research,formed a set of congestion event detection method based on video at nighttime highway,and to design and implementation under the platform of VS2010 and OpenCV2.4.8,and experiments were carried out using night video data collected on the spot of highway.The experimental results show that,the method can accurately extract the target of the lamp,and in the premise of real-time algorithm,improved the accuracy of congestion state discriminant at night under the actual scene.