Active video object extraction
Ye Lü, Ze-Nian Li · 2005
This paper addresses the problem of intelligently extracting objects from videos. Our method assumes that the video making process is purposive and attempts to extract those objects that the original authors of the videos intended to capture. We accomplish this by analyzing three types of actions of the author (saccadic movements, smooth pursuits, and multi-baseline pursuits) in an active vision framework using dense 2D disparity vectors computed from successive frames of the video. We demonstrate the effectiveness of our algorithm using real video sequences.