Алгоритм идентификации аномальных действий
(Стр. 64-80)
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Хади Намир Мохамед
ассистент, кафедра компьютерной и информационной безопасности
МИРЭА – Российский технологический университет
г. Москва, Российская Федерация Андрюшенков Дмитрий Геннадьевич ассистент, кафедра компьютерной и информационной безопасности; МИРЭА – Российский технологический университет; г. Москва, Российская Федерация Чесалин Александр Николаевич кандидат технических наук; заведующий, кафедра компьютерной и информационной безопасности; МИРЭА – Российский технологический университет; г. Москва, Российская Федерация
МИРЭА – Российский технологический университет
г. Москва, Российская Федерация Андрюшенков Дмитрий Геннадьевич ассистент, кафедра компьютерной и информационной безопасности; МИРЭА – Российский технологический университет; г. Москва, Российская Федерация Чесалин Александр Николаевич кандидат технических наук; заведующий, кафедра компьютерной и информационной безопасности; МИРЭА – Российский технологический университет; г. Москва, Российская Федерация
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Аннотация:
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.
Образец цитирования:
ОБРАЗЕЦ ЦИТИРОВАНИЯ: Хади Н.М., Андрюшенков Д.Г., Чесалин А.Н. Алгоритм идентификации аномальных действий // Computational Nanotechnology. 2024. Т. 11. № 3. С. 64-80. DOI: 10.33693/2313-223X-2024-11-3-64-80. EDN: QHUGEP
Список литературы:
Aberkane 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).
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).
Ключевые слова:
глубокое обучение, поведение человека, видеонаблюдение.
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