卷 45, 编号 4 (2019)

The importance of the topic of this paper is caused by the rapid development of virtual, mixed, and augmented reality systems. A problem in designing virtual reality (VR), augmented reality (AR) and mixed reality (MR) systems is the assessment of the visual perception of the result, in particular, the assessment of discomfort caused either by differences in the illumination conditions of objects in the real and virtual worlds or by the conflict of the vergence and accommodation of vision of the device user. The standard optical characteristics, such as aberration, frequency and contrast characteristic, and others are not applicable to evaluating the discomfort of visual perception. On the other hand, the discomfort can be assessed using a real proptotype; however, its fabrication can be labor consuming and expensive. We propose methods for virtual prototyping of visual optical systems that can be used in designing AR, VR, and MR devices for the analysis of discomfort caused, for example, by the vergence–accommodation conflict in virtual and augmented reality systems or by differences in the illumination conditions of real and virtual scene objects in mixed reality systems.

The problem of tracking people in a video stream with the aim of counting them is studied. Modern video surveillance systems, such as the Moscow video surveillance system, use hundreds of thousands of cameras. The use of modern methods developed for working on a single computer with an expensive graphical processor is economically inefficient for such large-scale systems. In this paper, a distributed tracking algorithm is proposed. It makes it possible to reduce the amount of computational resources due to detecting people in a sparse set of frames. The detection is performed on servers installed in a data center, while the video stream is processed by local camera computation nodes. The experimental evaluation showed that the proposed algorithm provides acceptable quality at the detection rate of 4/3 Hz.

This paper is devoted to the development of tools that allow museum workers to create interactive exhibits by using the Internet of Things (IoT) technology. We propose a unified software and hardware solution that enables the users who do not have deep knowledge in electronics and programming to assemble and interconnect various devices by the principle of a construction set. This, in turn, makes it possible to implement the concept of Smart Museum without the involvement of third-party IT specialists and large investments. The hardware part of the proposed solution is based on the ESP8266 programmable microcontroller with a built-in WiFi module. Peripheral devices can be connected to this microcontroller via expansion boards, the so-called shields. The software part of the solution is based on the SciVi adaptive multiplatform scientific visualization system. This system is fully controlled by the ontological knowledge base and provides the user with a high-level graphical interface that allows him or her to describe visualization algorithms by using data flow diagrams (DFDs). In this work, the SciVi system is supplemented with a mechanism that automatically generates firmware for IoT devices. It is based on an electronic component ontology that describes these devices and their programming methods. The firmware code generator controlled by this ontology automates the creation of lightweight embedded SciVi copies, which are installed on IoT devices and act as servers for data collection, processing, and visualization. To test the proposed solution, we created an interactive exhibit of Dimetrodon grandis, an extinct species of synapsids that lived during the Early Permian.

An algorithm of methods selection for processing multidimensional heterogeneous data based on the general properties of the data used and the methods included in the review is proposed in this paper. The algorithm is implemented in the form of software and a group of interpolation algorithms is compared by the example of the problem of constructing a 3D human face model for a person’s recognition system. It is shown that the proposed algorithm for selecting data processing methods works successfully for a group of data interpolation methods.

Human gait is an important biometric index that allows to identify a person at a great distance without direct contact. Due to these qualities, which other popular identifiers such as fingerprints or iris do not have, the recognition of a person by the manner of walking has become very common in various areas where video surveillance systems can be used. With the development of computer vision techniques, a variety of approaches for human identification by movements in a video appear. These approaches are based both on natural biometric characteristics (human skeleton, silhouette, and their change during walking) and abstract features trained automatically which do not have physical justification. Modern methods combine classical algorithms of video and image analysis and new approaches that show excellent results in related tasks of computer vision, such as human identification by face and appearance or action and gesture recognition. However, due to the large number of conditions that can affect the walking manner of a person itself and its representation in video, the problem of identifying a person by gait still does not have a sufficiently accurate solution. Many methods are overfitted by the conditions presented in the databases on which they are trained, which limits their applicability in real life. In this paper, we provide a survey of state-of-the-art methods of gait recognition, their analysis and comparison on several popular video collections and for different formulations of the problem of recognition. We additionally reveal the problems that prevent the final solution of gait identification challenge.