Title: Parameter Sensitive Detectors

Authors: Quan Yuan, Ashwin Thangali, Vitaly Ablavsky, Stan Sclaroff

Abstract:

Object detection can be challenging when the object class exhibits
large variations. One commonly-used strategy is to first partition
the space of possible object variations and then train separate
classifiers for each portion.  However, with continuous spaces the
partitions tend to be arbitrary since there are no natural boundaries
(for example, consider the continuous range of human body poses).  In
this paper, a new formulation is proposed, where the detectors
themselves are associated with continuous parameters, and reside in a
parameterized function space. There are two advantages of this
strategy. First, a-priori partitioning of the parameter space is not
needed; the detectors themselves are in a parameterized space.
Second, the underlying parameters for object variations can be
learned from training data in an unsupervised manner. For profile
face detection, our detection rate outperforms Viola-Jonesą method
by 5%, for 90 false alarms. On a hand shape data set, our method
improves detection rate from 98% to 99.5% at a false positive rate of
0.1%, compared with partition based methods. On a pedestrian data
set, our method reduces miss detection rate by a factor of three at a
false positive rate of 1%, compared with Dalal-Triggs method.