Event Location: NSH 4201
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.

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.