Parametric model for video content analysis
Pattern Recognition Letters
Shot boundary detection in MPEG videos using local and global indicators
IEEE Transactions on Circuits and Systems for Video Technology
Video shot boundary detection: Seven years of TRECVid activity
Computer Vision and Image Understanding
Video cut detection using dominant color features
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Reducing false positives in video shot detection using learning techniques
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
| Hi-index | 0.00 |
In content-based video analysis, shot boundary detection (SBD) is a common first step which segments video data into elementary shots, each comprising a sequence of consecutive frames recording a video event or scene continuous in time and space. Many SBD methods have been proposed in the literature, and experimental results show that the existing methods work reasonably well for abrupt shot boundaries, but less effectively for gradual shot boundaries. In this paper, we propose an effective post-refinement method for identifying actual shot boundaries from the results obtained by existing SBD methods. The proposed method formulates the SBD problem as sequential detection of changes in the underlying feature distributions whose parameters are estimated from existing video shots. Specifically, the proposed post-refinement method enhances the performance of SBD by identifying as many false positives (false detections) and false negatives (miss detections) as possible. Experiments conducted on a large set of test videos, whose initial shot boundaries are obtained by four existing SBD methods, show that the proposed post-refinement method can improve markedly the detection recall and precision and is rather insensitive to the thresholds used by the existing methods in detecting the initial shot boundaries.