云平台下压缩感知在交通监控视频中的研究

Research on compressed sensing in traffic monitoring video based on cloud platform

随着城市化进程的不断加快,海量的交通监控数据使得传统的存储技术遇到了瓶颈。压缩感知理论的出现为海量视频的压缩存储提供了全新思路,为了进一步解决压缩感知过程中信号重构求解数值优化时候的高计算度问题,将传统平台移至云平台借助其并行处理,进而加速压缩感知处理进程。云平台下基于压缩感知理论的交通监控视频处理框架,不仅能够提高提取速率,还能够在提高压缩率情况下,保证交通视频的输出质量。为智慧交通建设提供参考。

With the rapid development of urbanization,the traditional storage technology has encountered a bottleneck due to the massive traffic monitoring data.The emergence of compressed sensing theory provides a new idea for massive video compression storage.In order to further solve the problem of high computational complexity in signal reconstruction and numerical optimization in the process of compressed sensing,the traditional platform is moved to the cloud platform with its parallel processing,thus accelerating the process of compressed sensing processing.The traffic monitoring video processing framework based on compressed sensing theory in cloud platform can not only improve the extraction rate,but also ensure the output quality of traffic video with the increase of compression rate.It provides a reference for the construction of intelligent transportation.