A shot boundary detection method for news video based on object segmentation and tracking

As a critical step in many multimedia applications, shot boundary detection has attracted many research interests in recent years. The most of existing methods measure the similarity among video frames based on its low-level feathers. However, they are sensitive to the change in not only brightness, color, motion of object, but also camera motions and the quality of video. This paper proposes an innovative shot boundary detection method for news video based on video object segmentation and tracking. It combines three main techniques: the partitioned histogram comparison method, the video object segmentation and tracking based on wavelet analysis. The partitioned histogram comparison is used as the first filter to effectively reduce the number of video frames which need object segmentation and tracking. The unsupervised video object segmentation and tracking based on wavelet analysis is robust to those problems mentioned above. The efficacy of the proposed method is extensively tested with more than 3 hours of CCTV and CNN news programs, and that 96.4% recall with 97.2% precision have been achieved.