LOMO_XQDA

所屬分類：人工智能/神經網絡/深度學習

開發工具：matlab

文件大小：1129KB

下載次數：62

上傳日期：2016-06-12 21:12:17

上 傳 者：lorsson

說明：??行人重定位算法，識別效果非常好，有源碼和文章

(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)

文件列表：[舉報垃圾]

LOMO_XQDA

.........\bin

.........\...\Retinex.mexa64,28477,2015-05-05

.........\...\Retinex.mexglx,26658,2015-05-06

.........\...\Retinex.mexw32,29184,2015-05-04

.........\...\Retinex.mexw64,43520,2014-08-05

.........\code

.........\....\Demo_LOMO.m,868,2015-05-04

.........\....\Demo_XQDA.m,2188,2015-05-06

.........\....\EvalCMC.m,2069,2015-05-04

.........\....\LOMO.m,11237,2015-05-04

.........\....\MahDist.m,1091,2015-05-04

.........\....\SILTP.m,5268,2012-06-20

.........\....\XQDA.m,5660,2015-05-04

.........\data

.........\images

.........\Liao-CVPR15-LOMO-XQDA.pdf,1425131,2015-05-04

.........\LICENSE,387,2015-05-06

.........\README.txt,1929,2015-05-06

.........\results

.........\.......\cuhk01_lomo_xqda.mat,2712,2015-05-04

.........\.......\cuhk03_detected_lomo_xqda.mat,2712,2015-05-04

.........\.......\cuhk03_labeled_lomo_xqda.mat,2712,2015-05-04

.........\.......\qmul_grid_lomo_xqda.mat,2712,2015-05-04

.........\.......\qmul_grid_lomo_xqda_camera-network.mat,2712,2015-05-04

.........\.......\viper_lomo_xqda.mat,2712,2015-05-04

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