Detection Of Violent Video With Audio-video Features Based On MPEG-7

With the development of multimedia and network, there are a wide variety of video onthe network, in which many violent videos exist, and they affect children’s physical andmental health, but there isn’t an effective way to detect the violent videos. Traditionalmanual annotation method and video detection method using single-mode are not applied inthis information society. More and more people are concerned about automatic detection ofvideo based on audio-visual feature. It applies a convenient way for managing the growingnumber of videos.Good video detection system mainly depends on video feature extraction and detectionalgorithm. In feature extraction, the passage uses MPEG-7audio and visual descriptors todetect violent video. Through the studying of many MPEG-7descriptors, the new methodtargeted chosen the features about audio, time, color, texture, space, motion. Parts ofMPEG-7descriptors were added and improved: instantaneous feature of audio was added,motion intensity descriptor was customized, and a new method to extract dominant color ofvideo was proposed. In detection algorithm, BP neural network optimized by geneticalgorithms is used to fuse the audio and visual features, and detect the video category.Using the above method, this paper detects four kinds of videos which are violentvideo, cartoon, music and news. And higher recall and precision are showed. Experimentshows that these selected features are representative, discriminative and not only can reducethe data redundancy, but also describe audio information effectively and comprehensively.Fusion model of neural network is more robust. And the method of fusing audio and visualfeatures improves the effect of video detecting obviously.