CMU talk: Shape Constrained Figure-Ground Segmentation and Tracking

Special VASC Seminar
Thursday, January 8, 2009

Shape Constrained Figure-Ground Segmentation and Tracking
Zhaozheng Yin
Penn State University

Abstract:
To avoid drift problems during adaptive tracking, we must raise the level of abstraction at which the tracker represents its target. The goal must tracking "objects", not a box of pixels or a color distribution. If we can explicitly segment the foreground from background, it is possible to keep the adaptive model anchored on just the foreground pixels. In this talk, we discuss a shape constrained segmentation approach for tracking. Global object shape information is embedded into local graph links in a Conditional Random Field framework, thus the graph cut is attracted to occur around the figure-ground boundary. When treating tracking as a figure-ground segmentation problem, the precise foreground matte can help reduce pixel classification error during model adaptation. Meanwhile, the collected shape templates are useful to search for and recognize the same object after occlusion or tracking failure.

Bio:
Zhaozheng Yin is currently a PhD candidate in Robert Collins's vision group at Penn State, where his research interests include object segmentation, tracking, motion detection and feature selection/fusion. He received his BS degree from Tsinghua University, China, and his MS degree from the University of Wisconsin at Madison.