Detecting scene changes and activities in video databases

This paper presents an automated approach to detecting scene changes and activities which are meaningful to the user. We show that scene changes and activities may by treated as as a collection of motion discontinuities. We then discuss how this formulation call be used to transform video streams into features such as the sign of the Gaussian and mean curvature of spatiotemporal surfaces. The measurable features may be used to partition the video stream, mark occurrence in database timeline, and characterize shots in a video database. We present video segmentation experiments involving real video data