交通监控视频图像语义分割及其拼接方法

视频拼接是图像拼接的外延,在场景监控、目标识别等应用中发挥着重要作用。传统视频拼接算法多要求视频间具有较大重叠区域且特征点匹配过程中只顾及图像几何特征,当处理交通监控视频时,会因不同摄像头之间重叠区域极小或主光轴之间夹角较大而导致无法拼接或图像变形较大。为此,本文提出一种交通监控视频图像语义分割及其拼接方法。首先,利用边缘角度二阶差分直方图算法自动识别多视频交汇区域的正射影像,并将其作为拼接背景图像;然后,基于全卷积神经网络对正射影像和视频图像进行语义分割,提取图像中的交通专题语义;最后,以交通专题语义作为约束进行特征点匹配,将各个交通监控视频匹配至背景正射影像,实现监控区域视频拼接。采用山东省某市实际视频数据进行试验验证,结果表明对于重叠区域较小的监控视频,本文方法可获得较好地拼接图像,同时可有效提高特征点匹配的准确度。

Image semantic segmentation and stitching method of traffic monitoring video

As the extension of image stitching,video stitching plays an important role in scene monitoring,target recognition and so on.Traditional video stitching methods are mostly suitable for the videos with large overlapping regions and only geometric features of images are considered in feature matching.When dealing with traffic monitoring videos,existing methods often leads to stitching failure or large distortion because of the overlap region between different cameras is small and the angle between the main optical axes is large.Hence,an image semantic segmentation and stitching method of traffic monitoring video is proposed in this paper.First,the edge angular second-order difference histogram algorithm is proposed to recognize the orthophoto image automatically in the multi-video intersection area,and the orthophoto image is used as the stitching background image.Second,the orthophoto image and video image are segmented semantically based on fully convolutional network(FCN),and traffic thematic features are extracted separately.Finally,the traffic thematic features are used as constraints for feature point matching,and each traffic monitoring image is matched to the orthophoto image to realize regional video stitching.The experimental of real video data from a city in Shandong Province show that the proposed method obtain better stitching images for monitoring videos with smaller overlap areas,and effectively improve the accuracy of feature point matching.