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首页> 外文期刊>Journal of Residuals Science & Technology >Traffic Outlier Causal Interaction Analysis of Video Monitoring Data Stream based on Optical Flow Detection Method
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Traffic Outlier Causal Interaction Analysis of Video Monitoring Data Stream based on Optical Flow Detection Method

机译:基于光流检测方法的视频监控数据流流量离群因果相互作用分析

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This paper analyze and implement the Hadoop-based exception model to handle unusual patterns of moving objects' trajectories generally reflect abnormal traffic streams on road networks. In this paper, the video processing interface was selected for preprocess image data. This paper introduced intelligent system with independence system algorithm. The intelligent processing algorithm was used for extracting data of abnormal traffic incident. The algorithm do a detailed analysis of final system efficiency by comparing different video monitoring data. This is the first abnormal traffic video monitoring data processing by using Hadoop distributed systems. Multi-threaded distributed systems import video source of HDFS was introduced, the distributed Hadoop processing framework was constructed by processing a video interface model, the model extracted video monitoring data based on vehicle element track and record by extracting secondary event. The database retrieves filter was used for filtering a variety of proposed traffic joint. Traffic outlier causal interaction analysis was the future research in distributed environment for traffic surveillance data extraction.
机译:本文分析并实现了基于Hadoop的异常模型,以处理运动对象轨迹的异常模式,这些异常模式通常反映了道路网络上的异常交通流。在本文中,视频处理接口被选择用于预处理图像数据。介绍了具有独立系统算法的智能系统。采用智能处理算法提取交通异常事件数据。该算法通过比较不同的视频监控数据对最终系统效率进行了详细分析。这是使用Hadoop分布式系统进行的第一个异常交通视频监控数据处理。介绍了多线程分布式系统导入HDFS的视频源,通过处理视频接口模型构建了分布式Hadoop处理框架,该模型基于车辆要素的跟踪和记录,通过提取二次事件来提取视频监控数据。数据库检索过滤器用于过滤各种建议的交通接头。交通离群因果相互作用分析是分布式环境中交通监控数据提取的未来研究。

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