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Violent Crowd Flow Detection Using Deep Learning

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Intelligent Information and Database Systems (ACIIDS 2019)

Abstract

This research aims in detecting violent crowd flows in the context of Bangladesh. For this purpose, we have collected a dataset which includes both violent and non-violent crowd flows. Different deep learning algorithms and approaches have been applied on this dataset to detect scenarios which contain violence. Convolutional neural networks (CNN) and long short-term memory network (LSTM) based architectures have been experimented separately on this dataset and in combination as well. Moreover, a model that was already pre-trained on violent movie scenes has been used to leverage transfer learning which outperformed all other experimented approaches with an accuracy of 95.67%. Surprisingly, the sequence model alone or in combination with CNN has not performed well on this particular dataset. The proposed model is lightweight hence it can be deployed easily in any security systems consisting of CCTV cameras or unmanned aerial vehicles (UAVs).

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References

  1. 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

    Chapter  Google Scholar 

  2. Claire-Heilene, D.: VSD, a public dataset for the detection of violent scenes in movies: design, annotation, analysis and evaluation. In: The Handbook of Brain Theory and Neural Networks, vol. 3361 (1995)

    Google Scholar 

  3. Dai, Q., Tu, J., Shi, Z., Jiang, Y.G., Xue, X.: Fudan at MediaEval 2013: violent scenes detection using motion features and part-level attributes. In: MediaEval, October 2013

    Google Scholar 

  4. Zhang, T., Jia, W., Yang, B., Yang, J., He, X., Zheng, Z.: Mowld: a robust motion image descriptor for violence detection. Multimed. Tools Appl. 76(1), 1419–1438 (2017)

    Article  Google Scholar 

  5. 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

    Chapter  Google Scholar 

  6. 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 (CVPRW), pp. 1–6. IEEE, June 2012

    Google Scholar 

  7. De Souza, F., Pedrini, H.: Detection of violent events in video sequences based on census transform histogram. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 323–329. IEEE, October 2017

    Google Scholar 

  8. Mohammadi, S., Kiani, H., Perina, A., Murino, V.: Violence detection in crowded scenes using substantial derivative. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE, August 2015

    Google Scholar 

  9. Lyu, Y., Yang, Y.: Violence detection algorithm based on local spatio-temporal features and optical flow. In: 2015 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), pp. 307–311. IEEE, December2015

    Google Scholar 

  10. Xu, Y., Wen, J.: Detecting robbery and violent scenarios. In: 2013 Second International Conference on Robot, Vision and Signal Processing (RVSP), pp. 25–30. IEEE, December 2013

    Google Scholar 

  11. 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. IEEE, August 2017

    Google Scholar 

  12. Violent Scenes Dataset: Technicolor. https://www.technicolor.com/dream/research-innovation/violent-scenes-dataset

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Correspondence to Rashedur M. Rahman .

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Sumon, S.A., Shahria, M.T., Goni, M.R., Hasan, N., Almarufuzzaman, A.M., Rahman, R.M. (2019). Violent Crowd Flow Detection Using Deep Learning. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_53

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  • DOI: https://doi.org/10.1007/978-3-030-14799-0_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14798-3

  • Online ISBN: 978-3-030-14799-0

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