Insitu Evaluation of Tracking Algorithms using Time Reversed ChainsWu Hao, Aswin C. Sankaranarayanan, and Rama Chellappa |
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Abstract:
Automatic evaluation of visual tracking algorithms in the absence
of ground truth is a very challenging and important problem. In
the context of online appearance modeling, there is an additional
ambiguity involving the correctness of the appearance model. In
this paper, we propose a novel performance evaluation strategy for
tracking systems based on particle filter using a
time reversed Markov chain. Starting
from the latest observation, the time reversed chain is propagated
back till the starting time $t=0$ of the tracking algorithm. The
posterior density of the time reversed chain is also computed. The
distance between the posterior density of the time reversed chain
(at $t=0$) and the prior density used to initialize the tracking
algorithm forms the decision statistic for evaluation. It is postulated
that when the data is generated true to the underlying models, the
decision statistic takes a low value. We empirically demonstrate
the performance of the algorithm against various common failure
modes in the generic visual tracking problem.
Finally, we
derive a small frame approximation that allows for very efficient
computation of the decision statistic.
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| IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 2007 (pdf) |