Comparison of video sequences with histograms of motion patterns

Making efficient use of video information requires the development of a video signature and a similarity measure to rapidly identify similar videos in a huge database. Most of existing techniques to address this problem have focused on the uncompressed domain. However, decoding and analyzing of a video sequence are extremely time-consuming tasks. Since video data are usually available in compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for comparing video sequences that works in the compressed domain. The proposed method is based on recognizing motion patterns extracted from the video stream and their occurrence histogram is proven to be a powerful feature for describing the video content. Experiments on a TRECVID 2010 dataset show that our approach presents high accuracy relative to the state-of- the-art solutions and in a computational time that makes it suitable for large collections.