Computational nanotechnologyComputational nanotechnologyComputational nanotechnologyComputational nanotechnologyComputational nanotechnologyComputational nanotechnology2313-223X2587-9693YUR-VAK65148810.33693/2313-223X-2024-11-3-64-80QHUGEPARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И МАШИННОЕ ОБУЧЕНИЕResearch ArticleAlgorithm for identifying abnormal actionsАлгоритм идентификации аномальных действийhttps://orcid.org/0009-0000-7122-5942LEM-0157-20244079-2513KhadiNamir MohamedХадиНамир Мохамед
Russian Federation

assistant lecturer, Department of Computer and Information Security

ассистент, кафедра компьютерной и информационной безопасности

hadi@mirea.ruhttps://orcid.org/0009-0004-5927-97954113-2967AndryushenkovDmitry G.АндрюшенковДмитрий Геннадьевич
Russian Federation

assistant lecturer, Department of Computer and Information Security

ассистент, кафедра компьютерной и информационной безопасности

andryushenkov@mirea.ruhttps://orcid.org/0000-0002-1154-615157210931888D-8080-20194334-5520ChesalinAlexander N.ЧесалинАлександр Николаевич
Russian Federation

Cand. Sci. (Eng.), Head, Department of Computer and Information Security

кандидат технических наук, заведующий, кафедра компьютерной и информационной безопасности

chesalin@mirea.ruMIREA – Russian Technological UniversityМИРЭА – Российский технологический университет1508202411364800102202501022025Copyright ©; 2024, Yur-VAKCopyright ©; 2024, Юр-ВАК2024Yur-VAKЮр-ВАКhttps://journals.eco-vector.com/2313-223X/about/editorialPolicieshttps://journals.eco-vector.com/2313-223X/article/view/651488

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.

Исследование посвящено проблеме распознавания человеческой деятельности (Human Activity Recognition, HAR) и определения нормальных и аномальных действий в зависимости от ситуации. Автоматизированное обнаружение аномальных действий с помощью технологий компьютерного зрения и оперативное реагирование позволяют усовершенствовать работу служб быстрого реагирования, тем самым спасти человеческие жизни или пресечь правонарушения. В работе представлен всесторонний обзор методов распознавания человеческой деятельности и выявления аномальных действий на основе глубокого обучения. Исследуются различные классификации аномальных действий, и затем обсуждаются и анализируются методы глубокого обучения и нейросетевые архитектуры, используемые для обнаружения аномальных действий. На основе проведенного сравнительного анализа различных подходов предложен алгоритм распознавания человеческой активности и разработана нейронная сеть, которая определяет насильственные и ненасильственные действия с точностью 92,22% в 150 эпохах.

deep learninghuman behaviorvideo surveillanceглубокое обучениеповедение человекавидеонаблюдениеAberkane S., Elarbi M. Deep reinforcement learning for real-world anomaly detection in surveillance videos. URL: https://sci-hub.ru/10.1109/ispa48434.2019.8966795 (data of accesses: 28.01.2024).Agahian S., Negin F., Köse С. An efficient human action recognition framework with pose-based spatiotemporal features. URL: https://www.sciencedirect.com/science/article/pii/S2215098618312345?via%3Dihub (data of accesses: 02.02.2024).Al-Dhamari A., Sudirman R., Mahmood N.H. Transfer deep learning along with binary support vector machine for abnormal behavior detection. URL: https://www.researchgate.net/publication/340145184_Transfer_Deep_Learning_Along_With_Binary_Support_Vector_Machine_for_Abnormal_Behavior_Detection#fullTextFileContent (data of accesses: 24.01.2024).Amrani H., Micucci D., Paolo N. Unsupervised deep learning-based clustering for human activity recognition. URL: https://www.researchgate.net/publication/365337299_Unsupervised_Deep_Learning-based_clustering_for_Human_Activity_Recognition#fullTextFileContent (data of accesses: 27.01.2024).Contardo P., Tomassini S., Falcionelli N. et al. Combining a mobile deep neural network and a recurrent layer for violence detection in videos. URL: https://ceur-ws.org/Vol-3402/paper05.pdf (data of accesses: 25.01.2024).Duong H.-T., Le V.-T., Hoang V. Deep learning-based anomaly detection in video surveillance: A survey. URL: https://typeset.io/papers/deep-learning-based-anomaly-detection-in-video-surveillance-29l7zb9s (data of accesses: 26.01.2024).Elesawy M., Hussein M., El Massih M.A. Real life violence situations dataset. URL: https://www.kaggle.com/datasets/mohamedmustafa/real-life-violence-situations-dataset (data of accesses: 03.02.2024).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).