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With the rapid growth of surveillance cameras in many public places tomon-itor human activities such as in malls, streets, schools and, prisons,there is a strong demand for such systems to detect violence eventsautomatically. Au-tomatic analysis of video to detect violence is significantfor law enforce-ment. Moreover, it helps to avoid any social, economic andenvironmental damages. Mostly, all systems today require manual humansupervisors to de-tect violence scenes in the video which is inefficient andinaccurate. in this work, we interest in physical violence that involved twopersons or more. This work proposed a novel method to detect violence using afusion tech-nique of two significantly different convolutional neural networks(CNNs) which are AlexNet and SqueezeNet networks. Each network followed byseparate Convolution Long Short Term memory (ConvLSTM) to extract ro-bust andricher features from a video in the final hidden state. Then, making a fusionof 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-maxclassifier. The performance of the proposed method is evaluated using threestandard benchmark datasets in terms of detection accuracy: Hockey Fightdataset, Movie dataset and Violent Flow dataset. The results show an accuracyof 97%, 100%, and 96% respectively. A comparison of the results with the stateof the art techniques revealed the promising capability of the proposed methodin recognizing violent videos.

Detecting Violence in Video Based on Deep Features Fusion Technique