Detecting Violence in Video Based on Deep Features Fusion Technique

• URL: http://arxiv.org/abs/2204.07443v1
• Date: Fri, 15 Apr 2022 12:51:20 GMT
• Title: Detecting Violence in Video Based on Deep Features Fusion Technique
• Authors: Heyam M. Bin Jahlan and Lamiaa A. Elrefaei
• Abstract summary: This work proposed a novel method to detect violence using a fusion tech-nique of two convolutional neural networks (CNNs) The performance of the proposed method is evaluated using three standard benchmark datasets in terms of detection accuracy.
• Score: 0.30458514384586394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid growth of surveillance cameras in many public places to mon-itor human activities such as in malls, streets, schools and, prisons, there is a strong demand for such systems to detect violence events automatically. Au-tomatic analysis of video to detect violence is significant for law enforce-ment. Moreover, it helps to avoid any social, economic and environmental damages. Mostly, all systems today require manual human supervisors to de-tect violence scenes in the video which is inefficient and inaccurate. in this work, we interest in physical violence that involved two persons or more. This work proposed a novel method to detect violence using a fusion tech-nique of two significantly different convolutional neural networks (CNNs) which are AlexNet and SqueezeNet networks. Each network followed by separate Convolution Long Short Term memory (ConvLSTM) to extract ro-bust and richer features from a video in the final hidden state. Then, making a fusion of these two obtained states and fed to the max-pooling layer. Final-ly, features were classified using a series of fully connected layers and soft-max classifier. The performance of the proposed method is evaluated using three standard benchmark datasets in terms of detection accuracy: Hockey Fight dataset, Movie dataset and Violent Flow dataset. The results show an accuracy of 97%, 100%, and 96% respectively. A comparison of the results with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.

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