Abdali, A.M.R., Al-Tuma, R.F.: Robust real-time violence detection in video using CNN And LSTM. In: 2019 2nd Scientific Conference of Computer Sciences (SCCS), pp. 104–108, March 2019
Google Scholar
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005
Google Scholar
Ding, C., Fan, S., Zhu, M., Feng, W., Jia, B.: Violence detection in video by using 3D convolutional neural networks. In: Bebis, G., et al. (eds.) ISVC 2014. LNCS, vol. 8888, pp. 551–558. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-14364-4_53
CrossRef
Google Scholar
Ditsanthia, E., Pipanmaekaporn, L., Kamonsantiroj, S.: Video representation learning for CCTV-based violence detection. In: 2018 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), pp. 1–5, December 2018
Google Scholar
Hassner, T., Itcher, Y., Kliper-Gross, O.: Violent flows: real-time detection of violent crowd behavior. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6, June 2012
Google Scholar
Kanai, S., Fujiwara, Y., Iwamura, S.: Preventing gradient explosions in gated recurrent units. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS 2017, pp. 435–444. Curran Associates Inc., Red Hook (2017)
Google Scholar
Kanojia, G., Kumawat, S., Raman, S.: Exploring temporal differences in 3D convolutional neural networks (2019)
Google Scholar
Li, C., Zhu, L., Zhu, D., Chen, J., Pan, Z., Li, X., Wang, B.: End-to-end multiplayer violence detection based on deep 3D CNN. In: Proceedings of the 2018 VII International Conference on Network, Communication and Computing, ICNCC 2018, Taipei City, Taiwan, pp. 227–230. ACM, New York (2018)
Google Scholar
Mohamed, E., Mohamad, H., Massih, M.A.E.: Real life violence situations dataset. https://kaggle.com/mohamedmustafa/real-life-violence-situations-dataset. Accessed January 2020
Morales, G., Salazar-Reque, I., Telles, J., Díaz, D.: Detecting violent robberies in CCTV videos using deep learning. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 282–291. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19823-7_23
CrossRef
Google Scholar
Mt, S.: Increasing crimes vs. population density in megacities. Sociol. Criminol.-Open Access 4(1), 1–2 (2016)
MathSciNet
CrossRef
Google Scholar
Bermejo Nievas, E., Deniz Suarez, O., Bueno García, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 332–339. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23678-5_39
CrossRef
Google Scholar
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Google Scholar
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 568–576. Curran Associates, Inc. (2014)
Google Scholar
Soliman, M.M., Kamal, M.H., El-Massih Nashed, M.A., Mostafa, Y.M., Chawky, B.S., Khattab, D.: Violence recognition from videos using deep learning techniques. In: 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 80–85, December 2019. https://doi.org/10.1109/ICICIS46948.2019.9014714
Song, W., Zhang, D., Zhao, X., Yu, J., Zheng, R., Wang, A.: A novel violent video detection scheme based on modified 3D convolutional neural networks. IEEE Access 7, 39172–39179 (2019)
CrossRef
Google Scholar
Sudhakaran, S., Lanz, O.: Learning to detect violent videos using convolutional long short-term memory. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6, August 2017
Google Scholar
Sumon, S.A., Shahria, M.D.T., Goni, M.D.R., Hasan, N., Almarufuzzaman, A.M., Rahman, R.M.: Violent crowd flow detection using deep learning. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11431, pp. 613–625. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14799-0_53
CrossRef
Google Scholar
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. arXiv:1905.11946 [cs, stat], May 2019. arXiv: 1905.11946
Tang, Z., Shi, Y., Wang, D., Feng, Y., Zhang, S.: Memory visualization for gated recurrent neural networks in speech recognition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2736–2740, March 2017. https://doi.org/10.1109/ICASSP.2017.7952654
Ullah, F.U.M., Ullah, A., Muhammad, K., Haq, I.U., Baik, S.W.: Violence detection using spatiotemporal features with 3D convolutional neural network. Sensors (Basel, Switz.) 19(11), 2472 (2019)
CrossRef
Google Scholar
Xu, X., Wu, X., Wang, G., Wang, H.: Violent video classification based on spatial-temporal cues using deep learning. In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID), vol. 01, pp. 319–322, December 2018
Google Scholar
Zhou, P., Ding, Q., Luo, H., Hou, X.: Violent interaction detection in video based on deep learning. J. Phys: Conf. Ser. 844, 012044 (2017)
Google Scholar