Abstract:

In order to improve the ability of multimedia to analyze the security on Web and effectively filter the objectionable content,a violent video scene detection algorithm based on three-dimensional attention is proposed.Taking the 3D DenseNet as the backbone network,the algorithm first uses the P3D to extract low-level spatial-temporal feature information.Second,the SimAM attention module is introduced to calculate channel-spatial attention so as to enhance the feature of the key area in the video frame.Then,a transition layer with temporal attention is designed to highlight the feature of key frames in the video.In this way,the channel-spatial-temporal attention is formed to better detect violent scenes.In the experiments on violence detection,the accuracy reaches 98.75% and 100% on Hockey and Movies,which are small data sets with a single content,and 89.25% on RWF-2000,which is a large data set with a diverse content.Results show that the proposed algorithm can effectively improve the performance of violence detection with 3D attention.In the violent content localization detection experiment on data set VSD2014,the better performance further proves the effectiveness and generalization ability of the algorithm.

Key words: violence detection, deep learning, attention mechanism, pattern recognition, P3D, 3D-DenseNet