Abstract
Shot change detection is the initial step of video segmentation and indexing. There are two basic types of shot changes. One is the abrupt change or cut, and the other is the gradual shot transition. The variations of the video feature values in shot transitions are often disturbed by camera or object motions. In this paper, we exploit motion and illumination estimation in a video sequence to detect both abrupt and gradual shot changes. An iterative process is used to refine the generalized optical flow constraints step by step. Two robust measures, the likelihood ratio and the intensity variation monotony in the motion-compensated frames, are used for detecting abrupt changes and gradual transitions. We test the proposed algorithm on a number of video sequences in the TREC 2001 benchmark. The comparisons indicate that the proposed shot segmentation algorithm is competitive against the best existing algorithms.