In the field of dense crowd scene, in order to improve the defects of present anomaly detection methods in real-time performance and applicability, a real-time method was proposed based on the optical flow feature and Kalman filtering. Firstly, the global optical flow value was extracted as the movement feature. Then the Kalman filtering was used to process the global optical flow value. The residual was analyzed based on the assumption that the residual obeyed a Gauss distribution in normal condition which was validated by the hypothesis testing. Then the parameter of the residual probability distribution was calculated through the Maximum Likelihood (ML) estimation. Finally, under a certain confidence coefficient level, the confidence interval of normal condition and the judgment formula of abnormal condition were obtained, which could be used to detect the abnormal events. The experimental result shows that, for the videos with the size of 320×240, the average detection time of the proposed method can be as low as 0.023 s/frame and the accuracy can reach above 95%. As a result, the proposed method has high detection efficiency and good real-time performance.

In the field of information security risk evaluation, it is difficult for the traditional static evaluation methods to adapt to the dynamic topology of Mobile Ad hoc NETwork (MANET). In order to solve the problem, a new dynamic reevaluation model was proposed. In the proposed model, the whole system was abstracted into a topology which was comprised of components and access paths. The relations between components were abstracted into three kinds of association relations and four kinds of combination relations. In addition, the methods of security metrics under different relations were provided. When system changed, the influence range of its change and new relation types were determined by taking the changed component as a center. Under that condition, only adjacent components were reevaluated. Then, the new local and whole security metrics were obtained. The experimental results show that the proposed model has higher evaluation efficiency and can decrease evaluation cost greatly.