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Published Articles >> Table of Contents >> Abstract
Eighth IEEE Workshop on Applications of Computer Vision (WACV'07)
p. 10
A Drifting-proof Framework for Tracking and Online Appearance Learning
Tony X. Han, University of Illinois at Urbana-Champaign
Ming Liu, University of Illinois at Urbana-Champaign
Thomas S. Huang, University of Illinois at Urbana-Champaign
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WACV.2007.4
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| Abstract |
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In order to avoid the notorious drifting problem for tracking
system, a new integrated appearance learning framework is
proposed in this paper. Previous tracking frameworks with
appearance learning ability [3, 11] either require supervised
offline training or will fail inevitably if the tracker
locks on the background. While in our framework, no offline
training is required. Given the location of the object
in the first frame of the video sequence, we model the foreground
(the image patch containing the object)/background
difference as the transition cost in our tracking objective
function. An tracker based on Dynamic Programming (DP)
and template prediction [14] is carried out on the pixels
with high foreground-likelihood. The typical views (i.e. appearance
model) proposed by the tracker are used to initialize
the states of a Hidden Markov Model (HMM). With
the learned HMM, the tracking results and the appearance
model can be further refined until the video sequence and all
of these estimated parameters/hidden variables can be well
explained by the HMM. Through this iterative procedure,
typical views of the object, transition probabilities between
the typical views, and location of the object are simultaneously
estimated with strong confidence. The experiments
show that the proposed framework achieves fairly satisfied
results for several challenging video sequences and therefore
has many potential applications for video analysis.
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 Additional Information
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Citation:
Tony X. Han, Ming Liu, Thomas S. Huang,
"A Drifting-proof Framework for Tracking and Online Appearance Learning,"
wacv,
p. 10,
Eighth IEEE Workshop on Applications of Computer Vision (WACV'07),
2007
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