Skip to main content

Advertisement

Log in

Video Anomaly Detection Based on HSOE-FAST Modified Deep Neural Network

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

In recent times, video surveillance has become indispensable for public security, leveraging computer vision advancements to analyze and comprehend lengthy video feeds. Anomaly detection and classification stand out as crucial components of this technology. Anomaly detection's primary objective is to swiftly identify irregularities within a given timeframe. It is promising to use Deep Neural Network (DNN) approaches for anomaly detection because they combine the ideas of deep learning and reinforcement learning, enabling artificial agents to learn from and gain insights from real-world data. A modified DNN (Deep Neural Network) technique known as HSOE-FAST (Histo sigmoid of Orientation and Enthalpy with Fast Accelerated Segment Test) was proposed in this study. From the dataset, the input is obtained. Initially, the input is pre-processed using a Gaussian filter followed by the feature extraction using the HSOE-FAST method and finally classification is done using the modified DNN approach. Compared to other approaches, our suggested solution achieves an accuracy of almost 99% while overcoming the shortcomings of the existing methodologies.

.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The Avenue dataset is considered to be the input sample for Anomaly Detection.

References

  1. Munyua JG, Wambugu G, Njenga ST. A Survey of deep learning solutions for anomaly detection in surveillance videos. Int J Comput Info Technol. 2021;10(5):2279–0764.

    Google Scholar 

  2. Baradaran M. Deep learning based semi-supervised video anomaly detection. PhD diss.: University Laval, Canada; 2023.

    Google Scholar 

  3. Kiran BR, Thomas DM, Parakkal R. An overview of deep learning-based methods for unsupervised and semi-supervised anomaly detection in videos. J Imaging. 2018;4(2):36.

    Article  Google Scholar 

  4. Doshi K. Video anomaly detection: practical challenges for learning algorithms. PhD diss. University of South Florida. 2022.

  5. Kumaran SK, Dogra DP, Roy PP, and Mitra A. Video trajectory classification and anomaly detection using hybrid CNN-VAE. ar Xiv preprint arXiv:1812.07203. 2018.

  6. Zavrtanik V, Kristan M, Skočaj D. Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 2021;112: 107706.

    Article  Google Scholar 

  7. Doshi K, and Yilmaz Y. Rethinking video anomaly detection-a continual learning approach. In: Proceedings of the IEEE/CVF Winter Conference on applications of computer vision, 2022; pp. 3961–3970.

  8. Sabokrou M, Fayyaz M, Fathy M, Moayed Z, Klette R. Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes. Comput Vis Image Underst. 2018;172:88–97.

    Article  Google Scholar 

  9. Munir M, Chattha MA, Dengel A, Ahmed S. A comparative analysis of traditional and deep learning-based anomaly detection methods for streaming data. In: 2019 18th IEEE International Conference on Machine Learning and applications (ICMLA), 2019; pp. 561–566. IEEE.

  10. Chadha GS, Islam I, Schwung A, Ding SX. Deep convolutional clustering-based time series anomaly detection. Sensors. 2021;21(16):5488.

    Article  Google Scholar 

  11. Toshniwal A, Kavi M, Jayashree R. Overview of anomaly detection techniques in machine learning. In: 2020 fourth International Conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC), 2020; pp. 808–815. IEEE.

  12. Pang G, Shen C, Cao L, Hengel AVD. Deep learning for anomaly detection: A review. ACM Comput Surv (CSUR). 2021;54(2):1–38.

    Article  Google Scholar 

  13. Al-amri R, Murugesan RK, Man M, Abdulateef AL, Al-Sharafi M, Alkahtani AA. A review of machine learning and deep learning techniques for anomaly detection in IoT data. Appl Sci. 2021;11(12):5320.

    Article  Google Scholar 

  14. Nayak R, Pati UC, Das SK. A comprehensive review on deep learning-based methods for vide o anomaly detection. Image Vis Comput. 2021;106: 104078.

    Article  Google Scholar 

  15. Nawaratne R, Alahakoon D, Silva DD, Yu X. Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Trans Industr Inf. 2019;16(1):393–402.

    Article  Google Scholar 

  16. Mohammadi B, Mahmood F, Sabokrou M. Image/video deep anomaly detection: a survey. arXiv preprint arXiv:2103.01739 2021.

  17. Mansour RF, Escorcia-Gutierrez J, Gamarra M, Jair AV, Leal N. Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model. Image Vis Comput. 2021;112:104229.

    Article  Google Scholar 

  18. Amin SUI, Sareer M, Ullah M, Sajjad M, Cheikh FA, Hijji M, Hijji A, Muhammad K. EADN: an efficient deep learning model for anomaly detection in videos. Mathematics. 2022;10(9):1555.

    Article  Google Scholar 

  19. Wang Z, Zou Y, Zhang Z. Cluster attention contrast for video anomaly detection. In: Proceedings of the 28th ACM International Conference on multimedia, 2020; pp. 2463–2471.

  20. Yu G, Wang S, Cai Z, Zhu E, Chuanfu X, Jianping Y, Kloft M. Cloze test helps: Effective video anomaly detection via learning to complete video events. In: Proceedings of the 28th ACM International Conference on multimedia, 2020; pp. 583–591.

  21. Prabha B, Shanker NR, Priya M, Ganesh E. Human anomalous activity detection: shape and motion approach in crowded scenes. J Phys Conf Ser. 2021;1921(1):012074.

    Article  Google Scholar 

  22. Chang Y, Tu Z, Xie W, Luo B, Zhang S, Sui H, Yuan J. Video anomaly detection with spatio-temporal dissociation. Pattern Recognit. 2022;122:108213. https://doi.org/10.1016/j.patcog.2021.108213.

    Article  Google Scholar 

  23. Baradaran M, Bergevin R. Object class aware video anomaly detection through image translation. In: 2022 19th Conference on Robots and Vision (CRV), Toronto, ON, Canada, 2022; pp. 90–97. https://doi.org/10.1109/CRV55824.2022.00020.

  24. Gunale KG, Mukherji P. Deep learning with a spatiotemporal descriptor of appearance and motion estimation for video anomaly detection. J Imaging. 2018;4(6):79.

    Article  Google Scholar 

  25. Iovane G, Ingenito G, Leone M. Motion tracking using fuzzy logic and consistent labeling for multiple objects in multiple cameras vision. J Discrete Math Sci Cryptogr. 2009;12(1):1–42.

    Article  Google Scholar 

  26. Girdhar P, Johri P, Virmani D. Incept_LSTM: Accession for human activity concession in automatic surveillance. J Discrete Math Sci Cryptogr. 2022;25(8):2259–73.

    Article  Google Scholar 

  27. Amrutha K, Prabu P. Effortless and beneficial processing of natural languages using transformers. Journal of Discrete Mathematical Sciences and Cryptography. 2022;25(7):1987–2005.

    Article  Google Scholar 

  28. Murthy NS, Jainuddin SK. An improved dark channel prior based defogging algorithm for video sequences. J Inf Optim Sci. 2021;42(1):29–39.

    Google Scholar 

  29. Abul-Huda B, Abu-Rukah Y. Application of multi-media database system in detection and expectation of groundwater quality degradation: a case study-North Jordan. J Inf Optim Sci. 2000;21(2):289–304.

    Google Scholar 

  30. Khan T, Singh K, Shariq M, Ahmad K, Savita KS, Ahmadian A, Conti M. An efficient trust-based decision-making approach for WSNs: Machine learning-oriented approach. Comput Commun. 2023;209:217–29.

    Article  Google Scholar 

  31. Nayak R, Pati UC, Das SK. A comprehensive review on deep learning-based methods for video anomaly detection. Image Vis Comput. 2021;106:104078.

    Article  Google Scholar 

  32. Dong F, Zhang Yu, Nie X. Dual discriminator generative adversarial network for video anomaly detection. IEEE Access. 2020;8:88170–6.

    Article  Google Scholar 

  33. Wu P, Liu J, Li M, Sun Y, Shen F. Fast sparse coding networks for anomaly detection in videos. Pattern Recogn. 2020;107: 107515.

    Article  Google Scholar 

  34. Sarker MI, Losada-Gutiérrez C, Marron-Romera M, Fuentes-Jiménez D, Luengo-Sánchez S. Semi-supervised anomaly detection in video-surveillance scenes in the wild. Sensors. 2021;21(12):3993.

    Article  Google Scholar 

  35. Avola D, Cannistraci I, Cascio M, Cinque L, Diko A, Fagioli A, Foresti GL, et al. A novel gan-based anomaly detection and localization method for aerial video surveillance at low altitude. Remote Sens. 2022;14(16):4110.

    Article  Google Scholar 

  36. Ekanayake EMCL, Lei Y, Li C. Crowd density level estimation and anomaly detection using multicolumn multistage bilinear convolution attention network (MCMS-BCNN-Attention). Appl Sci. 2022;13(1):248.

    Article  Google Scholar 

  37. Duong H-T, Le V-T, Hoang VT. Deep learning-based anomaly detection in video surveillance: a survey. Sensors. 2023;23(11):5024.

    Article  Google Scholar 

  38. Santhosh KK, Dogra DP, Roy PP, Mitra A. Vehicular trajectory classification and traffic anomaly detection in videos using a hybrid CNN-VAE Architecture. IEEE Trans Intell Transp Syst. 2021;23(8):11891–902.

    Article  Google Scholar 

  39. Zhang Qi, Han R, Xin G, Liu CH, Wang G, Chen LY. Lightweight and accurate DNN-based anomaly detection at edge. IEEE Trans Parallel Distrib Syst. 2021;33(11):2927–42.

    Article  Google Scholar 

  40. Zheng Z, Liu W, Liu R, Wang L, Mao L, Qiu Q, Ling G. Anomaly detection of metro station tracks based on sequential updatable anomaly detection framework. IEEE Trans Circuits Syst Video Technol. 2022;32(11):7677–91.

    Article  Google Scholar 

Download references

Funding

No.

Author information

Authors and Affiliations

Authors

Contributions

Anil Kumar Gupta and Rupak Sharma came up with the idea for the study; Anil Kumar Gupta, Rupak Sharma, and Rudra Pratap Ojha analyzed and interpreted the results; and Anil Kumar Gupta, Rupak Sharma, and Rudra Pratap Ojha prepared the draft paper. After reviewing the findings, all authors gave their approval to the manuscript's final draft.

Corresponding author

Correspondence to Anil Kumar Gupta.

Ethics declarations

Conflict of Interest

The corresponding author declares that there is no conflict of interest on behalf of all authors.

Research Involving Human and /or Animals

No human participants, human material, human data, or animals were involved in this research.

Informed Consent

Not Applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, A.K., Sharma, R. & Ojha, R.P. Video Anomaly Detection Based on HSOE-FAST Modified Deep Neural Network. SN COMPUT. SCI. 5, 588 (2024). https://doi.org/10.1007/s42979-024-02945-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-02945-8

Keywords

Navigation