Introduction - If you have any usage issues, please Google them yourself
Person re-identification is an important technique towards
automatic search of a person’s presence in a surveillance
video. Two fundamental problems are critical for
person re-identification, feature representation and metric
learning. An effective feature representation should be robust
to illumination and viewpoint changes, and a discriminant
metric should be learned to match various person images.
In this paper, we propose an effective feature representation
called Local Maximal Occurrence (LOMO), and
a subspace and metric learning method called Cross-view
Quadratic Discriminant Analysis (XQDA). The LOMO feature
analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation
against viewpoint changes. Besides, to handle illumination
variations, we apply the Retinex transform and a
scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional
subspace by cross-vi