Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model
Recently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim o...
- Autores:
-
Mansour, Romany F.
Escorcia-García, José
Gamarra, Margarita
VILLANUEVA, JAIR ASIR
Leal, Nallig
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8394
- Acceso en línea:
- https://hdl.handle.net/11323/8394
https://doi.org/10.1016/j.imavis.2021.104229
https://repositorio.cuc.edu.co/
- Palabra clave:
- Video surveillance
Intelligent systems
Anomaly detection
Deep reinforcement learning
UCSD dataset
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| title |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| spellingShingle |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model Video surveillance Intelligent systems Anomaly detection Deep reinforcement learning UCSD dataset |
| title_short |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| title_full |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| title_fullStr |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| title_full_unstemmed |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| title_sort |
Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model |
| dc.creator.fl_str_mv |
Mansour, Romany F. Escorcia-García, José Gamarra, Margarita VILLANUEVA, JAIR ASIR Leal, Nallig |
| dc.contributor.author.spa.fl_str_mv |
Mansour, Romany F. Escorcia-García, José Gamarra, Margarita VILLANUEVA, JAIR ASIR Leal, Nallig |
| dc.subject.eng.fl_str_mv |
Video surveillance Intelligent systems Anomaly detection Deep reinforcement learning UCSD dataset |
| topic |
Video surveillance Intelligent systems Anomaly detection Deep reinforcement learning UCSD dataset |
| description |
Recently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim of anomaly detection is to automatically determine the existence of abnormalities in a short time period. Deep reinforcement learning (DRL) techniques can be employed for anomaly detection, which integrates the concepts of reinforcement learning and deep learning enabling the artificial agents in learning the knowledge and experience from actual data directly. With this motivation, this paper presents an Intelligent Video Anomaly Detection and Classification using Faster RCNN with Deep Reinforcement Learning Model, called IVADC-FDRL model. The presented IVADC-FDRL model operates on two major stages namely anomaly detection and classification. Firstly, Faster RCNN model is applied as an object detector with Residual Network as a baseline model, which detects the anomalies as objects. Besides, deep Q-learning (DQL) based DRL model is employed for the classification of detected anomalies. In order to validate the effective anomaly detection and classification performance of the IVADC-FDRL model, an extensive set of experimentations were carried out on the benchmark UCSD anomaly dataset. The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively. |
| publishDate |
2021 |
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2021-06-22T19:45:58Z |
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2021-06-22T19:45:58Z |
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2021 |
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2023 |
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Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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Text |
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info:eu-repo/semantics/acceptedVersion |
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0262-8856 |
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https://hdl.handle.net/11323/8394 |
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https://doi.org/10.1016/j.imavis.2021.104229 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
[1] X. Zhang, S. Yang, J. Zhang, W. Zhang Video anomaly detection and localization using motion-field shape description and homogeneity testing Pattern Recogn., 105 (2020), p. 107394 [2] S. Veluchamy, L.R. Karlmarx, K.M. Mahesh Detection and localization of abnormalities in surveillance video using timerider-based neural network Comput. J. (2021), Article bxab002, 10.1093/comjnl/bxab002 [3] Y. Fan, G. Wen, D. Li, S. Qiu, M.D. Levine, F. Xiao Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder Comput. Vis. Image Underst., 195 (2020), p. 102920 [4] A. Alam, M.N. Khan, J. Khan, Y.K. Lee Intellibvr-intelligent large-scale video retrieval for objects and events utilizing distributed deep-learning and semantic approaches 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 28-35), IEEE (2020, February) [5] S. Liu, J. Tang Modified deep reinforcement learning with efficient convolution feature for small target detection in vhr remote sensing imagery ISPRS International Journal of Geo-Information, 10 (3) (2021), p. 170 CrossRefView Record in ScopusGoogle Scholar [6] S.K. Lakshmanaprabu, S.N. Mohanty, S. Krishnamoorthy, J. Uthayakumar, K. Shankar Online clinical decision support system using optimal deep neural networks Appl. Soft Comput., 81 (2019), p. 105487 [7] J. Uthayakumar, N. Metawa, K. Shankar, S.K. Lakshmanaprabu Intelligent hybrid model for financial crisis prediction using machine learning techniques Information Systems and e-Business Management, pp. (2018), pp. 1-29 View Record in ScopusGoogle Scholar [8] R. Hinami, T. Mei, S. Satoh Joint detection and recounting of abnormal events by learning deep generic knowledge IEEE International Conference on Computer Vision (ICCV) (2017) [9] R.T. Ionescu, F.S. Khan, M.-I. Georgescu, L. Shao Object-centric auto-encoders and dummy anomalies for abnormal event detection in video The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019) [10] W. Luo, W. Liu, S. Gao A revisit of sparse coding based anomaly detection in stacked RNN framework 2017 IEEE International Conference on Computer Vision (ICCV) (2017), pp. 341-349 [11] M. Sabokrou, M. Fathy, M. Hoseini Video anomaly detection and localization based on the sparsity and reconstruction error of auto-encoder Electron. Lett., 52 (13) (2016), pp. 1122-1124 [12] M. Ravanbakhsh, M. Nabi, H. Mousavi, E. Sangineto, N. Sebe Plug-and-play CNN for crowd motion analysis: An application in anomalous event detection WACV (2017) [13] Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Reinhard Klette Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes Comp. Vision Image Underst., 172 (2018), pp. 88-97 [14] M. Hasan, J. Choi, J. Neumanny, A.K. Roy-Chowdhury, L.S. Davis Learning Temporal Regularity in Video Sequences CVPR (2016) [15] D. Xu, E. Ricci, Y. Yan, J. Song, N. Sebe Learning Deep Representations of Appearance and Motion for Anomalous Event Detection BMVC (2015), pp. 1-12 [16] M. Bellver, X. Giro-i-Nieto, F. Marques, J. Torres Hierarchical Object Detection with Deep Reinforcement Learning Proceedings of the Conference on Neural Information Processing Systems, Barcelona, Spain (December 2016), pp. 5-20 [17] X. Kong, B. Xin, Y. Wang, G. Hua Collaborative deep reinforcement learning for joint object search Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA (21–26 July 2017), pp. 7072-7081 [18] B. Uzkent, C. Yeh, S. Ermon Efficient object detection in large images using deep reinforcement learning Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA (1–5 March 2020), pp. 1824-1833 [19] S. Liu, D. Huang, Y. Wang Pay attention to them: deep reinforcement learning-based Cascade object detection IEEE Trans Neural Netw. Learn Syst., 31 (2020), pp. 2544-2556 [20] S. Ren, K. He, R. Girshick, J. Sun Faster R-CNN: towards real-time object detection with region proposal networks IEEE Trans. Pattern Anal. Mach. Intell., 39 (6) (2015), pp. 1137-1149 [21] A.A. Micheal, K. Vani Automatic object tracking in optimized UAV video J. Supercomput., 75 (8) (2019), pp. 4986-4999 [22] X. Lei, Z. Sui Intelligent fault detection of high voltage line based on the faster R-CNN Measurement, 138 (2019), pp. 379-385 [23] Y. Ding, L. Ma, J. Ma, M. Suo, L. Tao, Y. Cheng, C. Lu Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: a deep reinforcement learning approach Adv. Eng. Inform., 42 (2019), p. 100977 [24] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis Human-level control through deep reinforcement learning Nature, 518 (2015), pp. 529-533 [25] http://www.svcl.ucsd.edu/projects/anomaly/dataset.html [26] B.S. Murugan, M. Elhoseny, K. Shankar, J. Uthayakumar Region-based scalable smart system for anomaly detection in pedestrian walkways Computers & Electrical Engineering, 75 (2019), pp. 146-160 |
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Mansour, Romany F.Escorcia-García, JoséGamarra, MargaritaVILLANUEVA, JAIR ASIRLeal, Nallig2021-06-22T19:45:58Z2021-06-22T19:45:58Z202120230262-8856https://hdl.handle.net/11323/8394https://doi.org/10.1016/j.imavis.2021.104229Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Recently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim of anomaly detection is to automatically determine the existence of abnormalities in a short time period. Deep reinforcement learning (DRL) techniques can be employed for anomaly detection, which integrates the concepts of reinforcement learning and deep learning enabling the artificial agents in learning the knowledge and experience from actual data directly. With this motivation, this paper presents an Intelligent Video Anomaly Detection and Classification using Faster RCNN with Deep Reinforcement Learning Model, called IVADC-FDRL model. The presented IVADC-FDRL model operates on two major stages namely anomaly detection and classification. Firstly, Faster RCNN model is applied as an object detector with Residual Network as a baseline model, which detects the anomalies as objects. Besides, deep Q-learning (DQL) based DRL model is employed for the classification of detected anomalies. In order to validate the effective anomaly detection and classification performance of the IVADC-FDRL model, an extensive set of experimentations were carried out on the benchmark UCSD anomaly dataset. The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively.Mansour, Romany F.Escorcia-García, José-will be generated-orcid-0000-0002-4746-9047-600Gamarra, Margarita-will be generated-orcid-0000-0003-1834-2984-600VILLANUEVA, JAIR ASIR-will be generated-orcid-0000-0002-5672-2243-600Leal, Nallig-will be generated-orcid-0000-0002-4913-8540-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Image and Vision Computinghttps://www.sciencedirect.com/science/article/pii/S0262885621001347Video surveillanceIntelligent systemsAnomaly detectionDeep reinforcement learningUCSD datasetIntelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning modelPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion[1] X. Zhang, S. Yang, J. Zhang, W. Zhang Video anomaly detection and localization using motion-field shape description and homogeneity testing Pattern Recogn., 105 (2020), p. 107394[2] S. Veluchamy, L.R. Karlmarx, K.M. Mahesh Detection and localization of abnormalities in surveillance video using timerider-based neural network Comput. J. (2021), Article bxab002, 10.1093/comjnl/bxab002[3] Y. Fan, G. Wen, D. Li, S. Qiu, M.D. Levine, F. Xiao Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder Comput. Vis. Image Underst., 195 (2020), p. 102920[4] A. Alam, M.N. Khan, J. Khan, Y.K. Lee Intellibvr-intelligent large-scale video retrieval for objects and events utilizing distributed deep-learning and semantic approaches 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 28-35), IEEE (2020, February)[5] S. Liu, J. Tang Modified deep reinforcement learning with efficient convolution feature for small target detection in vhr remote sensing imagery ISPRS International Journal of Geo-Information, 10 (3) (2021), p. 170 CrossRefView Record in ScopusGoogle Scholar[6] S.K. Lakshmanaprabu, S.N. Mohanty, S. Krishnamoorthy, J. Uthayakumar, K. Shankar Online clinical decision support system using optimal deep neural networks Appl. Soft Comput., 81 (2019), p. 105487[7] J. Uthayakumar, N. Metawa, K. Shankar, S.K. Lakshmanaprabu Intelligent hybrid model for financial crisis prediction using machine learning techniques Information Systems and e-Business Management, pp. (2018), pp. 1-29 View Record in ScopusGoogle Scholar[8] R. Hinami, T. Mei, S. Satoh Joint detection and recounting of abnormal events by learning deep generic knowledge IEEE International Conference on Computer Vision (ICCV) (2017)[9] R.T. Ionescu, F.S. Khan, M.-I. Georgescu, L. Shao Object-centric auto-encoders and dummy anomalies for abnormal event detection in video The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)[10] W. Luo, W. Liu, S. Gao A revisit of sparse coding based anomaly detection in stacked RNN framework 2017 IEEE International Conference on Computer Vision (ICCV) (2017), pp. 341-349[11] M. Sabokrou, M. Fathy, M. Hoseini Video anomaly detection and localization based on the sparsity and reconstruction error of auto-encoder Electron. Lett., 52 (13) (2016), pp. 1122-1124[12] M. Ravanbakhsh, M. Nabi, H. Mousavi, E. Sangineto, N. Sebe Plug-and-play CNN for crowd motion analysis: An application in anomalous event detection WACV (2017)[13] Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Reinhard Klette Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes Comp. Vision Image Underst., 172 (2018), pp. 88-97[14] M. Hasan, J. Choi, J. Neumanny, A.K. Roy-Chowdhury, L.S. Davis Learning Temporal Regularity in Video Sequences CVPR (2016)[15] D. Xu, E. Ricci, Y. Yan, J. Song, N. Sebe Learning Deep Representations of Appearance and Motion for Anomalous Event Detection BMVC (2015), pp. 1-12[16] M. Bellver, X. Giro-i-Nieto, F. Marques, J. Torres Hierarchical Object Detection with Deep Reinforcement Learning Proceedings of the Conference on Neural Information Processing Systems, Barcelona, Spain (December 2016), pp. 5-20[17] X. Kong, B. Xin, Y. Wang, G. Hua Collaborative deep reinforcement learning for joint object search Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA (21–26 July 2017), pp. 7072-7081[18] B. Uzkent, C. Yeh, S. Ermon Efficient object detection in large images using deep reinforcement learning Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA (1–5 March 2020), pp. 1824-1833[19] S. Liu, D. Huang, Y. Wang Pay attention to them: deep reinforcement learning-based Cascade object detection IEEE Trans Neural Netw. Learn Syst., 31 (2020), pp. 2544-2556[20] S. Ren, K. He, R. Girshick, J. Sun Faster R-CNN: towards real-time object detection with region proposal networks IEEE Trans. Pattern Anal. Mach. Intell., 39 (6) (2015), pp. 1137-1149[21] A.A. Micheal, K. Vani Automatic object tracking in optimized UAV video J. Supercomput., 75 (8) (2019), pp. 4986-4999[22] X. Lei, Z. Sui Intelligent fault detection of high voltage line based on the faster R-CNN Measurement, 138 (2019), pp. 379-385[23] Y. Ding, L. Ma, J. Ma, M. Suo, L. Tao, Y. Cheng, C. Lu Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: a deep reinforcement learning approach Adv. Eng. Inform., 42 (2019), p. 100977[24] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis Human-level control through deep reinforcement learning Nature, 518 (2015), pp. 529-533[25] http://www.svcl.ucsd.edu/projects/anomaly/dataset.html[26] B.S. Murugan, M. Elhoseny, K. Shankar, J. Uthayakumar Region-based scalable smart system for anomaly detection in pedestrian walkways Computers & Electrical Engineering, 75 (2019), pp. 146-160PublicationORIGINALIntelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model.pdfIntelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model.pdfapplication/pdf80599https://repositorio.cuc.edu.co/bitstreams/baf3c5cf-9d08-47eb-9ebf-d79d16c46af5/downloadd7f2ceced00bcd9a894ab777ba96a4f6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/1a571feb-3d70-48a3-a331-8dbb0708350a/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/4dd63155-5b71-433f-aa27-25ab0ac4f30f/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILIntelligent video anomaly detection and classification using faster RCNN 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