Motion Field and Optical Flow: Qualitative Properties
IEEE Transactions on Pattern Analysis and Machine Intelligence
Production model based digital video segmentation
Multimedia Tools and Applications
Video parsing and browsing using compressed data
Multimedia Tools and Applications
Geometry of Distorted Visual Space and Cremona Transformation
International Journal of Computer Vision
Performance Characterization and Comparison of Video Indexing Algorithms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Schematic storyboarding for video visualization and editing
ACM SIGGRAPH 2006 Papers
Annotating and navigating tourist videos
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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A key step for managing a large video database is to partition the video sequences into shots. Past approaches to this problem tend to confuse gradual shot changes with changes caused by smooth camera motions. This is in part due to the fact that camera motion has not been dealt with in a more fundamental way. We propose an approach that is based on a physical constraint used in optical flow analysis, namely, the total brightness of a scene point across two frames should remain constant if the change across two frames is a result of smooth camera motion. Since the brightness constraint would be violated across a shot change, the detection can be based on detecting the violation of this constraint. It is robust because it uses only the qualitative aspect of the brightness constraint—detecting a scene change rather than estimating the scene itself. Moreover, by tapping on the significant know-how in using this constraint, the algorithm's robustness is further enhanced. Experimental results are presented to demonstrate the performance of various algorithms. It was shown that our algorithm is less likely to interpret gradual camera motions as shot changes, resulting in a significantly better precision performance than most other algorithms.