  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.
