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Reseach Article

Anomaly Detection in Surveillance Video of Natural Environment

by Silas Santiago L. Pereira, Jose E.B. Maia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 1
Year of Publication: 2021
Authors: Silas Santiago L. Pereira, Jose E.B. Maia
10.5120/ijca2021921288

Silas Santiago L. Pereira, Jose E.B. Maia . Anomaly Detection in Surveillance Video of Natural Environment. International Journal of Computer Applications. 183, 1 ( May 2021), 1-7. DOI=10.5120/ijca2021921288

@article{ 10.5120/ijca2021921288,
author = { Silas Santiago L. Pereira, Jose E.B. Maia },
title = { Anomaly Detection in Surveillance Video of Natural Environment },
journal = { International Journal of Computer Applications },
issue_date = { May 2021 },
volume = { 183 },
number = { 1 },
month = { May },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number1/31889-2021921288/ },
doi = { 10.5120/ijca2021921288 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:32.095956+05:30
%A Silas Santiago L. Pereira
%A Jose E.B. Maia
%T Anomaly Detection in Surveillance Video of Natural Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 1
%P 1-7
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work demonstrates the effectiveness of the median filter combined with morphological operators in the detection of anomalies in video surveillance of scenes of natural environment. Natural environment is characterized by backgrounds that are not static but whose dynamics are limited and do not include the appearance or disappearance of background objects in the scene. Examples include background images with seawater or river surfaces, or landscapes with trees, in which the wind produces waves and other movements of limited amplitude. The performance on four publicly available benchmark videos is compared to that of other published state-of-the-art works. The results obtained are promising.

References
  1. Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K Warmuth. Occam’s razor. Information processing letters, 24(6):377–380, 1987.
  2. Emmanuel J Cand`es, Xiaodong Li, Yi Ma, and John Wright. Robust principal component analysis? Journal of the ACM (JACM), 58(3):1–37, 2011.
  3. KG Manosha Chathuramali, Sameera Ramasinghe, and Ranga Rodrigo. Abnormal activity recognition using spatiotemporal features. In 7th International Conference on Information and Automation for Sustainability, pages 1–5. IEEE, 2014.
  4. Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati. Detecting moving objects, ghosts, and shadows in video streams. IEEE transactions on pattern analysis and machine intelligence, 25(10):1337–1342, 2003.
  5. Nir Friedman and Stuart Russell. Image segmentation in video sequences: A probabilistic approach. arXiv preprint arXiv:1302.1539, 2013.
  6. Belmar Garcia-Garcia, Thierry Bouwmans, and Alberto Jorge Rosales Silva. Background subtraction in real applications: Challenges, current models and future directions. Computer Science Review, 35:100204, 2020.
  7. Charles Guyon, Thierry Bouwmans, El-hadi Zahzah, et al. Robust principal component analysis for background subtraction: Systematic evaluation and comparative analysis. Principal component analysis, 10:223–238, 2012.
  8. Olga Isupova. Machine learning methods for behaviour analysis and anomaly detection in video. Springer, Cham, Switzerland, 2018.
  9. Tangqing Li, Zheng Wang, Siying Liu, and Wen-Yan Lin. Deep unsupervised anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3636–3645, 2021.
  10. Xinxin Li, Michael K Ng, and Xiaoming Yuan. Median filtering-based methods for static background extraction from surveillance video. Numerical Linear Algebra with Applications, 22(5):845–865, 2015.
  11. Davide A Migliore, Matteo Matteucci, and Matteo Naccari. A revaluation of frame difference in fast and robust motion detection. In Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pages 215– 218, 2006.
  12. Anurag Mittal and Nikos Paragios. Motion-based background subtraction using adaptive kernel density estimation. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., volume 2, pages II–II. Ieee, 2004.
  13. Erkki Oja. Principal components, minor components, and linear neural networks. Neural networks, 5(6):927–935, 1992.
  14. H´elio Pedrini andWilliam Robson Schwartz. An´alise de imagens digitais: Princ´ipios. Algoritmos e Aplicac¸ ˜oes, Thomsom, 2008.
  15. Pramuditha Perera, Poojan Oza, and Vishal M Patel. One-class classification: A survey. arXiv preprint arXiv:2101.03064, 2021.
  16. TJ Narendra Rao, GN Girish, and Jeny Rajan. An improved contextual information based approach for anomaly detection via adaptive inference for surveillance application. In Proceedings of International Conference on Computer Vision and Image Processing, pages 133–147. Springer, 2017.
  17. Christof Ridder, Olaf Munkelt, and Harald Kirchner. Adaptive background estimation and foreground detection using kalman-filtering. In Proceedings of International Conference on recent Advances in Mechatronics, pages 193–199. Citeseer, 1995.
  18. Mehrsan Javan Roshtkhari and Martin D Levine. An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Computer vision and image understanding, 117(10):1436–1452, 2013.
  19. Baidya Nath Saha, Nilanjan Ray, and Hong Zhang. Snake validation: A pca-based outlier detection method. IEEE Signal Processing Letters, 16(6):549–552, 2009.
  20. BoyangWan, Yuming Fang, Xue Xia, and Jiajie Mei.Weakly supervised video anomaly detection via center-guided discriminative learning. In 2020 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2020.
  21. Guojun Yin, Bin Liu, Huihui Zhu, Tao Gong, and Nenghai Yu. A large scale urban surveillance video dataset for multiple-object tracking and behavior analysis. arXiv preprint arXiv:1904.11784, 2019.
  22. Wang Zhou. Image quality assessment: from error measurement to structural similarity. IEEE transactions on image processing, 13:600–613, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Anomaly detection background extraction median filter morphology operator intelligent video surveillance