Improvement of Shot Detection Using Illumination Invariant Metric and Dynamic Threshold Selection

Abstract

Automatic shot detection is the first step and also an important step for content-based parsing and indexing of video data. Many methods have been introduced to address this problem, e.g. pixel-by-pixel comparisons and histogram comparisons. But gray or color histograms used in most existing methods ignore the problem of illumination variation inherent in the video production process. So they often fail when the incident illumination varies. And because shot change is basically a local process of a video, it is difficult to find an appropriate global threshold for absolute difference measure. In this paper, new techniques for shot detection are proposed. We use color ratio histograms as frame content measure, because it is robust to illumination changes. A local adaptive threshold technique is adopted to utilize the local characteristic of shot change. The effectiveness of our methods is validated by experiments on some real-world video sequences. Some experimental results are also discussed in this paper.