基于深度学习的铁路客站视频融合智能监控系统的图像处理优化技术研究

针对复杂铁路客站现场全景视频高维特征缺乏、融合匹配不准确等问题，提出一种基于深度学习的铁路客站视频融合智能监控系统的图像处理优化技术。文章通过尺度不变特征变换算法检测出图像关键点，利用卷积神经网络进行高维特征提取，对错配点使用随机抽样一致性算法进行剔除，并对虚影问题进行了优化以获得更好的细节效果。提出的图像处理优化技术已应用于连云港—镇江高速铁路扬州东站。应用结果表明，该技术能有效防止图片失真，获得更好的拼接效果。

Image processing optimization technology of railway passenger station video fusion intelligent monitoring system based on deep learning

In view of the lack of high-dimensional features and inaccurate fusion and matching of panoramic video of complex railway passenger station, this paper proposed an image processing optimization technology of railway passenger station video fusion intelligent monitoring system based on deep learning. In this paper, scale invariant feature transformation algorithm was used to detect the key points of the image, convolution neural network was used to extract the high-dimensional features, random sampling consistency algorithm was used to eliminate the mismatch points, and the phantom problem was optimized to obtain better details effect. The proposed image processing optimization technology has been applied to Yangzhou East Station of Lianyungang-Zhenjiang high-speed railway. The application results show that this technology can effectively prevent image distortion and obtain better mosaic effect.