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A novel algorithm for complexity reduction in binary image processing, namely for computation of correlation between image and object template is proposed. This algorithm is based on direct Computation of vector-matrix multiplication with utilisation of binary matrix factorisation approach. Comparison with other algorithms is givenand it is shown that our approach allows to reduce time and complexily of this task.

Ensuring public safety is an important issue for all developed countries of the world. In this area, various organizations conduct constant monitoring, as a result of which ratings are published, the results of which have a significant impact on choosing a place of residence, attracting tourists and investors, etc. To simplify control of public safety in cities, artificial intelligence systems are increasingly used. Along with the positive effects of such systems, the protection of human privacy is becoming a key issue. This article describes the results of Belarusian scientists on the creation of such systems and examines the features of their implementation in a “smart city” in our country. The experience of a number of countries in the implementation of artificial intelligence systems to strengthen public safety in existing "smart cities" is analyzed. The legal problems arising during the functioning of such systems are considered, especially in terms of limiting the freedom and rights of citizens. Proposals are given on the development of the regulatory framework in the Republic of Belarus in order to protect the rights of citizens.

This paper discusses the algorithmic framework for tracking people on indoor video. To improve tracking accuracy was used face identification algorithm to reduce errorr rate during complicated trajectory of persons in indoor environment. Object detection was performed with CNN Yolov3 that extract rectangular area as a result. Face detection task was resolved eith Cascade CNN MTCNN with following recognition using CNN MobileFaceNetwork. To form person features we used historgrams in HSV colorspave and CNN that includes 29 convolution layers followed by fully connected layer. The Hungarian algorithm was used as decision maker for allignment problem. Experiments were conducted on five videosequences with the variable number of people in it. The main characteristics of the developed algorithm are obtained which confirmed its effectiveness and the possibility of use for indoor video surveillance.

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.