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CNN-BiLSTM Model for Violence Detection in Smart Surveillance

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Abstract

In this paper, a lightweight computational model has been introduced for the better classification of violent and non-violent activities. Nowadays, the occurrences of violent activities get increased in public places. It has various social and economic reasons behind the growth of violent actions. Therefore, the government agencies and public administrators need to check such incidents using smart surveillance. Deep learning-based an efficient violent activity detection model can help the authorities in detecting a violent activity in real-time. The evaluated results can be henceforth sent to store and analyze the captured video to automate the crime monitoring system. Convolutional Neural Network-based Bidirectional LSTM has been used to detect violent activities and also compared with other existing approaches. Our proposed model gives 99.27%, 100% and 98.64% classification accuracies for the widely used standard Hockey Fights, Movies and Violent-Flows video datasets, respectively.

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Notes

  1. https://www.tensorflow.org/.

  2. https://keras.io/.

  3. https://www.numpy.org/.

  4. N=total number of frames/n, n=10, x=100, y=100, c=3.

  5. https://github.com/tintybot/CNN-BiLSTM-Model.

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Authors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, 751024, Odisha, India

    Rohit Halder & Rajdeep Chatterjee

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  1. Rohit Halder
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  2. Rajdeep Chatterjee
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Correspondence to Rajdeep Chatterjee.

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Halder, R., Chatterjee, R. CNN-BiLSTM Model for Violence Detection in Smart Surveillance. SN COMPUT. SCI. 1, 201 (2020). https://doi.org/10.1007/s42979-020-00207-x

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