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Discrete-Continuous Optimization for Multi-Target Tracking (CVPR 2012)

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Published on Mar 1, 2012

Code available: http://goo.gl/rkKXN

The problem of multi-target tracking is comprised of two distinct, but
tightly coupled challenges: (i) the naturally discrete problem of data
association, \ie assigning image observations to the appropriate
target; (ii) the naturally continuous problem of trajectory
estimation, \ie recovering the trajectories of all targets. To go
beyond simple greedy solutions for data association, recent approaches
often perform multi-target tracking using discrete optimization. This
has the disadvantage that trajectories need to be pre-computed or
represented discretely, thus limiting accuracy. In this paper we
instead formulate multi-target tracking as a discrete-continuous
problem that handles each aspect in its natural domain and allows one
to leverage powerful algorithms for multi-model fitting. Data
association is performed using discrete optimization with label costs,
and can be solved to near optimality. Trajectory estimation is posed
as a continuous fitting problem with a simple closed-form solution,
which is used in turn to update the label costs. We demonstrate the
accuracy and robustness of our
approach with state-of-the-art performance on several standard
datasets.

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