基于视频图像检测的高速公路车型分道行驶监测系统

为解决交通监管部门对于高速公路客货混流问题监管效率低效果差的问题,通过视频图像检测法对高速公路进行研究和应用,构建了基于机器学习和计算机视觉的视频图像检测模式,以提高视频检测的稳定性和准确率,提出了基于尺度不变特征变换(scale invariant feature transformation,SIFT)池化的车辆特征提取模型,摒除传统视频背景建模稳定性和准确率不高的缺陷,获取车辆车型特征数据和分道行驶参数,经过试点样本训练后,实验结果表明:车型识别的准确率高达95％以上,车辆分道检测的准确率达到90％左右.

Monitoring System of Freeway Vehicle Lane Separation based on Video Image Detection

Highway passenger and cargo mixed operation is an important incentive for traffic accidents. According to statistics, more than 10 people died in traffic accidents, and the collision between trucks and buses accounted for 75%. Although both home and abroad have begun to promote the passenger and freight division, but the supervision of the passenger and freight traffic is not perfect, the current supervision means is also mainly camera photography, later by the staff on the violation of the inspection, and the lack of a mature intelligent detection scheme about the passenger and cargo separation. A video image detection method is used to study and apply to the expressway. In order to improve the accuracy and stability of video detection, a video image detection model based on computer vision and depth learning is built in this paper. The Scale invariant feature transformation (SIFT) pool based on the scale invariant feature transform is proposed. The model of vehicle feature extraction was used to remove the shortcomings of traditional video background modeling with low stability and accuracy, and the feature data and running parameters of vehicles were obtained. After the pilot sample training, the experimental results show that the accuracy of vehicle recognition is as high as 95%, and the accuracy of vehicle lane detection can also reach about 90%.