Violence Detection from Video under 2D Spatio-Temporal Representations

Action recognition in videos, especially for violence detection, is now a hot topic in computer vision. The interest of this task is related to the multiplication of videos from surveillance cameras or live television content producing complex $2D+t$ data. State-of-the-art methods rely on end-to-end learning from 3D neural network approaches that should be trained with a large amount of data to obtain discriminating features. To face these limitations, we present in this article a method to classify videos for violence recognition purpose, by using a classical 2D convolutional neural network (CNN). The strategy of the method is two-fold: (1) we start by building several 2D spatio-temporal representations from an input video, (2) the new representations are considered to feed the CNN to the train/test process. The classification decision of the video is carried out by aggregating the individual decisions from its different 2D spatio-temporal representations. An experimental study on public datasets containing violent videos highlights the interest of the presented method.