Learning to Detect Violent Videos using Convolutional Long Short-Term Memory
This work addresses the need for automated violence detection in surveillance videos, which is of broad interest for security applications, but it is incremental as it builds on existing deep learning techniques.
The authors tackled the problem of automatically detecting violence in surveillance videos by proposing a deep neural network that combines a convolutional neural network with a convolutional long short-term memory to capture spatio-temporal features, achieving promising recognition accuracy compared to state-of-the-art methods on three benchmark datasets.
Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos. A convolutional neural network is used to extract frame level features from a video. The frame level features are then aggregated using a variant of the long short term memory that uses convolutional gates. The convolutional neural network along with the convolutional long short term memory is capable of capturing localized spatio-temporal features which enables the analysis of local motion taking place in the video. We also propose to use adjacent frame differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance of the proposed feature extraction pipeline is evaluated on three standard benchmark datasets in terms of recognition accuracy. Comparison of the results obtained with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.