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Automatic detection of dangerous situations in order to ensure the safety of residents is a new step in the development of video surveillance systems in cities. And dangerous situations are often caused by deviant behavior of people: robbery, brawl, vandalism and etc. But due to the strong variability of such scenes, their detection is a challenging problem, which still remains unresolved. The key to solving this problem is the recognition of fine-grained features and events of scenes and the application of knowledge management technologies. In this paper, three computer vision technologies for detecting people, tracking people and estimating three-dimensional human poses were integrated with the aim of recognizing the actions and interactions of people in three-dimensional space. For all technologies an open source implementations were used that showed high results in popular computer vision challenges. A dataset was also created using computer graphics to test the developed system, containing scenes of the interaction of people in the city, shot under different point of views. This dataset showed that additional teaching of the human pose estimation component to handle challenging poses of people and camera viewpoints is required.

When performing the internal audit of computer equipment an important problem is to identify the elf (executable and linkable format) files stored on the investigated hard drive. To solve this problem, we propose a method of identification of unknown elf-ffles based on the metric classification algorithms. The method consists of three stages. On the first stage the preparation of the training sample by the disassembly of each file and submitting it in the form of an ordered set of the 118 elements is implemented. Each of these elements is the frequency of occurrences of the 118 most commonly used commands in the assembler code. Each program in the sample is represented by several sets, corresponding to different versions or operating systems in which this software is installed. Then, the Minkowski metric of each sample file and identifiable file is calculated. For the method of potential functions the selection of the reference elements of each set is obtained. On the third stage using the metric classification algorithms we evaluate affiliation of the identifiable file for a particular program from the sample. To approbate proposed method the experiment with the use of this method was conducted; results showing the accuracy of identification of elf-files was equal to 89,60% were obtained. The results indicate that this method is applicable in problems of identification of elf-files while conducting the internal audit of computer equipment. The advantages of the method are the accuracy of program identification regardless of the elf-files versions in the Linux operating systems. The ease of implementation of our method and the identification execution speed can be used not only in tasks of internal audit, but also in other tasks of computer forensics.