行人重定位算法，识别效果非常好，有源码和文章-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