Recently, robust principal component analysis (RPCA) has been widely used in the detection of moving objects. However, this method fails to effectively utilize the low-rank prior information of the background and the spatiotemporal continuity prior of the moving object, and the target extraction effect is often poor when dealing with large-scale complex scenes. To solve the above problems, a new non-convex rank approximate RPCA model based on segmentation constraint is proposed. Firstly, the model adopts the low-rank sparse decomposition method to divide the original video sequence into three parts: low-rank background, moving foreground and sparse noise. Then, a new non-convex function is proposed to better constrain the low-rank characteristic of the video background. Finally, based on the spatiotemporal continuity of the foreground object, the video is segmented by the super-pixel segmentation technology, so as to realize the constraint of the motion foreground region. The augmented Lagrange multiplier method is used to solve the model. Experimental results show that the proposed model can effectively improve the accuracy of moving object detection, and has better visual effect of foreground object detection than existed methods. INDEX TERMS Moving object detection, robust principal component analysis, non-convex rank approximation, video segmentation.
Aiming at the terrorist attacks that have happened in 2015 and 2016, which have not been organized or claimed responsibility by individuals, the suspect prediction algorithm based on naive Bayesian method is used to solve the problem of finding the perpetrators of terrorist attacks. Firstly, we select five terrorist organizations and individuals which are more harmful to terrorist attacks. Then, we use the suspect prediction algorithm based on naive Bayesian method to select the terrorist attacks that occurred in 2015 and 2016, which have not yet been organized or claimed responsibility by individuals. Finally, we use the Sklearn machine learning library of Python to calculate and get all the suspects in each incident. The probability of a suspect is the degree of suspicion.
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