Violence detection using pre-trained models

Violence detection aims at recognizing whether a violent action has happened. This field gained widespread popularity since there is a need to find applicable and automatic violence detection methods which explored visual data received from surveillance cameras installed in different areas. In this paper, we employed pre-trained deep neural networks in order to present a low-complexity method for violence detection. The extracted features from pre-trained models have been pooled and fed into a fully connected network in order to detect whether a violent action has occurred. As pre-trained models, the results of both ResNet-50 and VGG16 are explored in the proposed approach. We evaluate the effectiveness of the method on four public datasets. The experimental results depict the efficiency of the low-complexity proposed approach in comparison with other approaches using time-consuming networks like recurrent ones.