This paper addresses automatic summarization of MPEG audiovisual content on compressed domain. By analyzing semantically important low-level and mid-level audiovisual features, our method universally summarizes the MPEG-1/-2 contents in the form of digest or highlight. The former is a shortened version of an original, while the latter is an aggregation of important or interesting events. In our proposal, first, the incoming MPEG stream is segmented into shots and the above features are derived from each shot. Then the features are adaptively evaluated in an integrated manner, and finally the qualified shots are aggregated into a summary. Since all the processes are performed completely on compressed domain, summarization is achieved at very low computational cost. The experimental results show that news highlights and sports highlights in TV baseball games can be successfully extracted according to simple shot transition models. As for digest extraction, subjective evaluation proves that meaningful shots are extracted from content without a priori knowledge, even if it contains multiple genres of programs. Our method also has the advantage of generating an MPEG-7 based description such as summary and audiovisual segments in the course of summarization.
KEYWORDS: Video, Video surveillance, Detection and tracking algorithms, Motion estimation, Motion analysis, Video compression, Cameras, Motion detection, Video processing, Data communications
We describe a method of moving object detection directly from MPEG coded data. Since motion information in MPEG coded data is determined in terms of coding efficiency point of view, it does not always provide real motion information of objects. We use a wide variety of coding information including motion vectors and DCT coefficients to estimate real object motion. Since such information can be directly obtained from coded bitstream, very fast operation can be expected. Moving objects are detected basically analyzing motion vectors and spatio-temporal correlation of motion in P-, and B-pictures. Moving objects are also detected in intra macroblocks by analyzing coding characteristics of intra macroblocks in P- and B-pictures and by investigating temporal motion continuity in I-pictures. The simulation results show that successful moving object detection has been performed on macroblock level using several test sequences. Since proposed method is very simple and requires much less computational power than the conventional object detection methods, it has a significant advantage as motion analysis tool.
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