Algorithm for identifying abnormal actions
( Pp. 64-80)
More about authors
Khadi Namir Mohamed
assistant lecturer, Department of Computer and Information Security
MIREA – Russian Technological University
Moscow, Russian Federation Andryushenkov Dmitry G. assistant lecturer, Department of Computer and Information Security; MIREA – Russian Technological University; Moscow, Russian Federation Chesalin Alexander N. Cand. Sci. (Eng.); Head, Department of Computer and Information Security; MIREA – Russian Technological University; Moscow, Russian Federation
MIREA – Russian Technological University
Moscow, Russian Federation Andryushenkov Dmitry G. assistant lecturer, Department of Computer and Information Security; MIREA – Russian Technological University; Moscow, Russian Federation Chesalin Alexander N. Cand. Sci. (Eng.); Head, Department of Computer and Information Security; MIREA – Russian Technological University; Moscow, Russian Federation
Abstract:
The study is devoted to the problem of recognition of human activity recognition and the definition of normal and abnormal behavior (activity) depending on the action scene. Automated detection of abnormal activity using computer vision technologies and rapid response makes it possible to improve the work of rapid response services, thereby saving human lives or stopping offenses. The paper presents a comprehensive review of methods for recognizing human activity and detecting abnormal human activity based on deep learning. Various classifications of abnormal activity are investigated, and then deep learning methods and neural network architectures used to detect abnormal activity are discussed and analyzed. Based on the comparative analysis of various approaches, an algorithm for recognizing human activity has been proposed and a neural network has been developed that determines violent and nonviolent actions with an accuracy of 92,22% in 150 epochs.
How to Cite:
Khadi N.M., Andryushenkov D.G., Chesalin A.N. Algorithm for identifying abnormal actions. Computational Nanotechnology. 2024. Vol. 11. No. 3. Pp. 64–80. (In Rus.). DOI: 10.33693/2313-223X-2024-11-3-64-80. EDN: QHUGEP
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Волков Н.Г. Нейролингвистическое программирование и основные концепции обучения в технологическом вузе и военно-учебном заведении // Вестник Казанского технологического университета. 2014. Т. 17. № 8. С. 315–322.
Qin H., Gong R., Liu X., Shen M. Forward and backward information retention for accurate binary neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Proceedings. 2020. Pp. 2257–2265.
Белов М.А., Гришко С.И., Живетьев А.В. и др. Применение методов нечеткой логики для формирования адаптивной индивидуальной траектории обучения на основе динамического управления сложностью курса // Моделирование, оптимизация и информационные технологии. 2022. № 10 (4).
Kahneman D. et al. Pupillary, heart rate, and skin resistance changes during a mental task // Journal of Experimental Psychology. 1969. No. 79. Pp. 164–167.
Albiero V., Chen X., Yin X., Pang G. img2pose: Face alignment and detection via 6DoF, face pose estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Proceedings. 2021. Pp. 755–764.
Есин Р.В., Зыкова Т.В., Кустицкая Т.А., Кытманов А.А. Цифровая образовательная история как составляющая цифрового профиля обучающегося в условиях трансформации образования // Перспективы науки и образования. 2022. № 5 (59). С. 566–584.
Gu T., Dolan-Gavitt B., Garg S. BadNets: Identifying vulnerabilities in the machine learning model supply chain. URL: https://arxiv.org/abs/1708.06733 (data of accesses: 03.02.2024).
Haq Qazi E.U., Zia T., Almorjan A. Deep learning-based digital image forgery detection system. URL: https://www.researchgate.net/publication/359153551 Deep Learning-Based Digital Image Forgery Detection System (data of accesses: 03.02.2024).
Jayaswal R., Dixit M. Framework for anomaly classification using deep transfer learning approach. URL: https://iieta.org/journals/ria/paper/10.18280/ria.350309 (data of accesses: 24.01.2024).
Jia C., Yi W., Wu Y. et al. Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition. URL: https://arxiv.org/abs/2006.15873 (data of accesses: 28.01.2024).
Jia J.-G., Zhou Y.-F., Hao X.-W. et al. Two-stream temporal convolutional networks for skeleton-based human action recognition. URL: https://sci-hub.ru/10.1007/s11390-020-0405-6 (data of accesses: 30.01.2024).
Lathifah N., Lin H.-I. A brief review on behavior recognition based on key points of human skeleton and eye gaze to prevent human error. In: Proceedings of the 2022 13th Asian Control Conference (ASCC). Jeju Island, Republic of Korea, 2022. Pp. 1396 1403.
Lin C.-B., Dong Z., Kuan W.-K., Huang Y.-F. A framework for fall detection based on openpose skeleton and LSTM/GRU models. URL: https://www.researchgate.net/publication/348142284 A Framework for Fall Detection Based on OpenPose Skeleton and LSTMGRU Models (data of accesses: 29.01.2024).
Lin F.-C., Ngo H.-H., Dow C.-R. et al. Student behavior recognition system. for the classroom environment based on skeleton pose estimation and person detection. URL: https://www.researchgate.net/publication/353746430 Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection (data of accesses: 30.01.2024).
Maqsood R., Bajwa UI., Saleem G. et al. Anomaly recognition from surveillance videos using 3D convolutional neural networks. URL: https://arxiv.org/pdf/2101.01073 (data of accesses: 03.02.2024).
Naik A., Gopalakrishna M. Deep-violence: Individual person violent activity detection in Video. URL: https://www.researchgate.net/publication/349393071 Deep-violence individual person violent activity detection in video (data of accesses: 29.01.2024).
Nauman M.A., Shoaib M. Identification of anomalous behavioral patterns in crowd scenes. URL: https://www.techscience.com/cmc/v71n1/45453/html (data of accesses: 29.01.2024).
Pang G., Shen C., Cao L., Hengel A. Deep learning for anomaly detection: A review. URL: https://ink.library.smu.edu.sg/cgi/viewcontent.cgi article 8019 context sis research (data of accesses: 26.01.2024).
Pawar K., Attar V. Deep learning approaches for video-based anomalous activity detection. URL: https://www.sci-hub.ru/10.1007/s11280-018-0582-1 (data of accesses: 26.01.2024).
Pimentel T., Monteiro M., Veloso A., Ziviani N. Deep active learning for anomaly detection. URL: https://sci-hub.ru/10.1109/ijcnn48605.2020.9206769 (data of accesses: 27.01.2024).
Simonyan K., Zisserman A. Two-stream convolutional networks for action recognition in videos. URL: https://arxiv.org/pdf/1406.2199 (data of accesses: 28.01.2024).
Sultani W., Chen C., Shah M. Real-world anomaly detection in surveillance videos. URL: https://paperswithcode.com/paper/real-world-anomaly-detection-in-surveillance (data of accesses: 03.02.2024).
Tomar S., Sharma A.K., Tina, Gupta K. Pose based activity recognition using supervised machine learning algorithms. URL: https://www.ijert.org/research/pose-based-activity-recognition-using-supervised-machine-learning-algorithms-IJERTV10IS120084.pdf (data of accesses: 27.01.2024).
Tran D., Bourdev L., Fergus R. et al. Learning spatiotemporal features with 3D convolutional networks. URL: https://arxiv.org/pdf/1412.0767 (data of accesses: 28.01.2024).
Tran D.A., Fischer P., Smajic A., So Y. Real-time object detection for autonomous driving using deep learning. URL: https://www.researchgate.net/publication/350090136 Real-time Object Detection for Autonomous Driving using Deep Learning (data of accesses: 26.01.2024).
Vrskova R., Hudec R., Kamencay P., Sykora P. A new approach for abnormal human activities recognition based on ConvLSTM architecture. URL: https://www.researchgate.net/publication/360159604 A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture (data of accesses: 28.01.2024).
Zhang F., Bazarevsky V., Vakunov A. et al. Mediapipe hands: On-device real-time hand tracking. URL: https://arxiv.org/pdf/2006.10214.pdf (data of accesses: 01.02.2024).
Zhao Y., Deng B., Shen Ch. et al. Spatio-temporal autoencoder for video anomaly detection. URL: https://sci-hub.ru/10.1145/3123266.3123451 (data of accesses: 28.01.2024).
Keywords:
deep learning, human behavior, video surveillance.
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