  This paper presents an active approach for part-based object detection, which
optimizes the order of part filter evaluations and the time at which to stop
and make a prediction. Statistics, describing the part responses, are learned
from training data and are used to formalize the part scheduling problem as an
offline optimization. Dynamic programming is applied to obtain a policy, which
balances the number of part evaluations with the classification accuracy.
During inference, the policy is used as a look-up table to choose the part
order and the stopping time based on the observed filter responses. The method
is faster than cascade detection with deformable part models (which does not
optimize the part order) with negligible loss in accuracy when evaluated on the
PASCAL VOC 2007 and 2010 datasets.
