Mining TV broadcasts for recurring video sequences
Ina Döhring, Rainer W. Lienhart · 2009
Abstract. Monitoring and analyzing TV broadcasts is an important task in the media as well as the advertising business. An important subtask is the frame-accurate detection of recurring video sequences. Examples of recurring video sequences are commercials, channel advertisements, channel intros, and newscast intros. Most of these different kinds of repeating video clips can automatically be classified by further analyzing their temporal and visual properties. In this work we introduce an algorithm and a real-time system for recognizing recurring video sequences frameaccurately in a highly effective and efficient manner. The algorithm does not require any temporal pre-segmentation by shot detection and can thus, in principle, be applied to any kind of temporal signal. It is frame-accurate, meaning that it exactly identifies with which frame a repeating sequence starts and ends. Thus, the temporal accuracy is 40 milliseconds for PAL and 33 milliseconds for NTSC videos. On a standard PC desktop a 24-hour live-stream can be processed in about 4 hours including the computational expensive video decoding. To achieve this efficiency the algorithm exploits an inverted index for identifying similar frames rapidly. Gradientbased