Abstract
The real-time events are fast and occurring at highly dynamical moments. Hence, the important challenges are identifying the anomaly incidents properly. The specified methods and techniques are to be quick in identification for control and other measures of the events. In the proposed method, the anomalies are detected from the surveillance videos using the multiple instance learning and ID3 for extracting the features. The extracted features are then used as input to a deep neural network where the classification of the videos to anomalous and normal videos is done. The investigated dataset is with 128 hours of videos with ten percent of different realistic anomaly videos. The AUC of the proposed approach is 81. The proposed approach is most beneficial for the real-world anomaly recognition in surveillance videos.
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References
H. Wei, K. Li, H. Li, Y. Lyu, X. Hu, Detecting video anomaly with a stacked convolutional LSTM framework. Lect Notes Comput Sci 2019;11754 LNCS, pp. 330–342
C. Wang, A. Musaev, P. Sheinidashtegol , T. Atkison, Towards detection of abnormal vehicle behavior using traffic cameras. Lect Notes Comput Sci 2019;11514 LNCS, pp. 125–136
P. Ahmadi, E.P. Moradian, I. Gholampour, Sequential topic modeling for efficient analysis of traffic scenes, in 9th International Symposium on Telecommunication: With Emphasis on Information and Communication Technology, IST 2018 (2019)
D. Thakur, R. Kaur, An optimized CNN based real world anomaly detection in surveillance videos. Int. J. Innov. Technol. Explor. Eng. 8(9 Special Issue), 465–473 (2019)
A. Joshi, V.P. Namboodiri, Unsupervised synthesis of anomalies in videos: transforming the normal, in Proceedings of the International Joint Conference on Neural Networks (2019)
P. Mehta, A. Kumar, S. Bhattacharjee, Fire and gun violence based anomaly detection system using deep neural networks, in Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020 (2020)
W. Ullah, A. Ullah, I.U. Haq, K. Muhammad, M. Sajjad, S.W. Baik, CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks. Multimedia Tools Appl. (2020)
K. Pawar, V. Attar, Deep learning-based intelligent surveillance model for detection of anomalous activities from videos. Int. J. Comput. Vis. Rob. 10(4), 289–311 (2020)
Y. Tang, L. Zhao, S. Zhang, C. Gong, G. Li, J. Yang, Integrating prediction and reconstruction for anomaly detection. Pattern Recogn. Lett. 129, 123–130 (2020)
B. Iqbal, W. Iqbal, N. Khan, A. Mahmood, A. Erradi, Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark. Cluster Comput. 23(1), 397–408 (2020)
S. Kisan, B. Sahu, A. Jena, S.N. Mohanty, Detection of violence in videos using hybrid machine learning techniques. Int. J. Adv. Sci. Technol. 29(3), 5386–5392 (2020)
A.K. Cherian, A. Rai, V. Jain, Flight trajectory prediction for air traffic management. J. Crit. Rev. 7(6), 412–416 (2020)
A.K. Cherian, P. Kumar, P.S.K. Reddy, E. Poovammal, Detecting bars in galaxies using convolutional neural networks. J. Crit. Rev. 7(6), 189–194 (2020)
D. Xu, E. Ricci, Y. Yan, J. Song, N. Sebe, Learning deep representations of appearance and motion for anomalous event detection, in BMVC (2015)
M. Hasan, J. Choi, J. Neumann, A.K. Roy-Chowdhury, L.S. Davis, Learning temporal regularity in video sequences, in CVPR (June 2016)
W. Sultani, C. Chen, M. Shah, Real-world anomaly detection in surveillance videos, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018)
https://www.dropbox.com/sh/75v5ehq4cdg5g5g/AABvnJSwZI7zXb8_myBA0CLHa?dl=0
J. Duchi, E. Hazan, Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. (2011)
J. Carreira, A. Zisserman, Quo vadis, action recognition? a new model and the kinetics dataset, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6299–6308
S. Xie, C. Sun, J. Huang, Z. Tu, K. Murphy, Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification, in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 305–321
X. Wang et al., I3D-LSTM: a new model for human action recognition, in IOP Conference Series: Materials Science and Engineering (2019)
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. (2014)
V. Nair, G.E. Hinton, Rectified linear units improve restricted boltzmann machines, in Proceedings of the 27 th International Conference on Machine Learning, Haifa, Israel (2010)
C. Lu, J. Shi, and J. Jia. Abnormal event detection at 150 fps in matlab, in ICCV (2013)
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Cherian, A.K., Poovammal, E. (2021). Anomaly Detection in Real-Time Surveillance Videos Using Deep Learning. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_19
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