Detecting pornographic video content by combining image features with motion information

With the rise of large-scale digital video collections, the challenge of automatically detecting adult video content has gained significant impact with respect to applications such as content filtering or the detection of illegal material. While most systems represent videos with keyframes and then apply techniques well-known for static images, we investigate motion as another discriminative clue for pornography detection. A framework is presented that combines conventional keyframe-based methods with a statistical analysis of MPEG-4 motion vectors. Two general approaches are followed to describe motion patterns, one based on the detection of periodic motion and one on motion histograms. Our experiments on real-world web video data show that this combination with motion information improves the accuracy of pornography detection significantly (equal error is reduced from 9.9% to 6.0%). Comparing both motion descriptors, histograms outperform periodicity detection.