In contrast to images, video sequences contain a much larger amount of information that varies both in space and time. People detection and behavior analysis using video sequences are important computer-vision problems that currently have an increasing number of applications, including activity recognition in surveillance and security systems. The results of the experiments are presented, including some examples of frames from processed video sequences with visualized trajectories for loitering and falls. To ensure a high accuracy of tracking and trajectory construction, the CNN features of the images are included in a composite descriptor, which also contains geometric and color features, to describe each person detected in the current frame and compare this person with all people detected in the next frame. For this problem, we generate a training dataset by combining the Kaggle dataset and a modified Wider Face dataset, in which face masks were superimposed on half of the images. Our approach to face-mask detection also uses YOLOv5 as a detector and classifier. Then, their faces in the selected regions are detected and the presence or absence of face masks is determined. At the first stage, people are detected using the YOLOv5 CNN and are marked with bounding boxes. Based on the proposed generalization, a detection and tracking algorithm that uses the tracking-by-detection paradigm and convolutional neural networks (CNNs) is developed. In this paper, we present a formalization of these problems. The automatic detection and tracking of appearance and behavior anomalies in video surveillance systems is one of the promising areas for the development and implementation of artificial intelligence.
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