Please use this identifier to cite or link to this item: https://elib.psu.by/handle/123456789/43122
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dc.contributor.authorChen, H.-
dc.contributor.authorBohush, R.-
dc.contributor.authorKurnosov, I.-
dc.contributor.authorMa, G.-
dc.contributor.authorWeichen, Y.-
dc.contributor.authorAblameyko, S.-
dc.date.accessioned2024-03-22T12:05:06Z-
dc.date.available2024-03-22T12:05:06Z-
dc.date.issued2022-
dc.identifier.citationChen, H., Bohush, R., Kurnosov, I. et al. Detection of Appearance and Behavior Anomalies in Stationary Camera Videos Using Convolutional Neural Networks. Pattern Recognit. Image Anal. 32, 254–265 (2022). https://doi.org/10.1134/S1054661822020067ru_RU
dc.identifier.urihttps://elib.psu.by/handle/123456789/43122-
dc.description.abstractThe 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. In this paper, we present a formalization of these problems. Based on the proposed generalization, a detection and tracking algorithm that uses the tracking-by-detection paradigm and convolutional neural networks (CNNs) is developed. At the first stage, people are detected using the YOLOv5 CNN and are marked with bounding boxes. Then, their faces in the selected regions are detected and the presence or absence of face masks is determined. Our approach to face-mask detection also uses YOLOv5 as a detector and classifier. 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. 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. The results of the experiments are presented, including some examples of frames from processed video sequences with visualized trajectories for loitering and falls.ru_RU
dc.language.isoenru_RU
dc.publisherSpringer Natureru_RU
dc.titleDetection of Appearance and Behavior Anomalies in Stationary Camera Videos Using Convolutional Neural Networksru_RU
dc.typeArticleru_RU
dc.identifier.doi10.1134/S1054661822020067-
Appears in Collections:Публикации в Scopus и Web of Science
Машинное обучение. Обработкой изображений и видео. Интеллектуальные системы. Информационная безопасность

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