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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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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