基于时空流量的监控视频运动片段分割  

Segmentation of Surveillance Video of Motion Segments Based on Spatiotemporal Flow

作　　者：张云佐 李汶轩 杨攀亮 Zhang Yunzuo;Li Wenxuan;Yang Panliang(School of Information Science and Technology,Shijiazhuang Tiedao University,Shijiazhuang,Hebei 050043,China)

机构地区：[1]石家庄铁道大学信息科学与技术学院,河北石家庄050043

出　　处：《激光与光电子学进展》2022年第8期154-162,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61702347,61972267);河北省自然科学基金(F2017210161);石家庄铁道大学研究生创新资助项目(YC2020070)。

摘　　要：监控视频运动片段分割是视频浓缩的基础和前提,现有的视频片段分割方法实现步骤复杂、计算量大,严重影响着视频浓缩的实时性能。针对此问题,提出了一种基于时空流量的监控视频运动片段分割方法。所提方法仅对监视区域边界像素进行稀疏采样,形成时空侧面;在此基础上,通过背景建模提取时空侧面中的目标;然后将运动目标进入和退出视觉监视区域建模成时空流量;最后根据目标特征匹配进行模型修正,得到累积时空流量曲线,并据此进行运动片段分割。实验结果表明,所提方法在保证视频分割准确性的同时,大大提高了视频运动片段分割效率。Segmentation of surveillance video into motion segments is the basis and premise of video synopsis.The existing video segment segmentation methods are complex in their implementation and computationally intensive,which has a detrimental effect on the realtime performance of video synopsis.To address these issues,a motion segment segmentation method of surveillance video based on the spatiotemporal flow model is proposed.The proposed technique only sparsely samples the boundary pixels of the video surveillance area to create the video spatiotemporal profile.On this basis,background modeling is used to extract the targets from the spatiotemporal profile.A spatiotemporal flow model for moving objects entering and exiting the visual surveillance area is subsequently constructed.Finally,the model is modified according to the moving target’s feature matching,and the accumulative spatiotemporal flow curve of the video is obtained,from which the motion segments are then segmented.Experimental results show that the presented method not only ensures video segmentation accuracy but also dramatically increases the speed of video motion segment segmentation.

关 键 词：图像处理 稀疏采样 时空侧面 时空流量 运动片段 监控视频 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]