The detection of abnormal vehicle events is a research hotspot in the analysis of highway surveillance video.Because of the complex factors,which include different conditions of weather,illumination,noise and so on,ve...The detection of abnormal vehicle events is a research hotspot in the analysis of highway surveillance video.Because of the complex factors,which include different conditions of weather,illumination,noise and so on,vehicle's feature extraction and abnormity detection become difficult.This paper proposes a Fast Constrained Delaunay Triangulation(FCDT) algorithm to replace complicated segmentation algorithms for multi-feature extraction.Based on the video frames segmented by FCDT,an improved algorithm is presented to estimate background self-adaptively.After the estimation,a multi-feature eigenvector is generated by Principal Component Analysis(PCA) in accordance with the static and motional features extracted through locating and tracking each vehicle.For abnormity detection,adaptive detection modeling of vehicle events(ADMVE) is presented,for which a semi-supervised Mixture of Gaussian Hidden Markov Model(MGHMM) is trained with the multi-feature eigenvectors from each video segment.The normal model is developed by supervised mode with manual labeling,and becomes more accurate via iterated adaptation.The abnormal models are trained through the adapted Bayesian learning with unsupervised mode.The paper also presents experiments using real video sequence to verify the proposed method.展开更多
分析了 Linux 操作系统调用信息在主机入侵检测系统中的应用;阐述了主机入侵检测系统 Host Keeper 中基于 Linux 系统调用传感器的原形框架、相关软件设计和实现方法,并着重探讨了用于二级系统调用的内核可加载模块传感器的实现。通过...分析了 Linux 操作系统调用信息在主机入侵检测系统中的应用;阐述了主机入侵检测系统 Host Keeper 中基于 Linux 系统调用传感器的原形框架、相关软件设计和实现方法,并着重探讨了用于二级系统调用的内核可加载模块传感器的实现。通过内核可加载模块传感器对系统运作关键数据信息的检测,以保证主机系统的安全。展开更多
基金Supported by the National Natural Science Foundation of China (Grant No.60803120)
文摘The detection of abnormal vehicle events is a research hotspot in the analysis of highway surveillance video.Because of the complex factors,which include different conditions of weather,illumination,noise and so on,vehicle's feature extraction and abnormity detection become difficult.This paper proposes a Fast Constrained Delaunay Triangulation(FCDT) algorithm to replace complicated segmentation algorithms for multi-feature extraction.Based on the video frames segmented by FCDT,an improved algorithm is presented to estimate background self-adaptively.After the estimation,a multi-feature eigenvector is generated by Principal Component Analysis(PCA) in accordance with the static and motional features extracted through locating and tracking each vehicle.For abnormity detection,adaptive detection modeling of vehicle events(ADMVE) is presented,for which a semi-supervised Mixture of Gaussian Hidden Markov Model(MGHMM) is trained with the multi-feature eigenvectors from each video segment.The normal model is developed by supervised mode with manual labeling,and becomes more accurate via iterated adaptation.The abnormal models are trained through the adapted Bayesian learning with unsupervised mode.The paper also presents experiments using real video sequence to verify the proposed method.
文摘分析了 Linux 操作系统调用信息在主机入侵检测系统中的应用;阐述了主机入侵检测系统 Host Keeper 中基于 Linux 系统调用传感器的原形框架、相关软件设计和实现方法,并着重探讨了用于二级系统调用的内核可加载模块传感器的实现。通过内核可加载模块传感器对系统运作关键数据信息的检测,以保证主机系统的安全。
