[1]刘翠响,袁香伟,王宝珠,等.最小均衡化后的行人重识别[J].深圳大学学报理工版,2019,36(No.5(473-600)):447-452.[doi:10.3724/SP.J.1249.2019.04447]
 LIU Cuixiang,YUAN Xiangwei,WANG Baozhu,et al.Minimum equalization for pedestrain re-identification[J].Journal of Shenzhen University Science and Engineering,2019,36(No.5(473-600)):447-452.[doi:10.3724/SP.J.1249.2019.04447]
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最小均衡化后的行人重识别()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第36卷
期数:
2019年No.5(473-600)
页码:
447-452
栏目:
【电子与信息科学】
出版日期:
2019-09-30

文章信息/Info

Title:
Minimum equalization for pedestrain re-identification
作者:
刘翠响袁香伟王宝珠张亚凤马杰
河北工业大学电子信息工程学院,天津 300401
Author(s):
LIU Cuixiang YUAN Xiangwei WANG Baozhu ZHANG Yafeng and MA Jie
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, P. R. China
关键词:
人工智能行人重识别特征融合下采样XQDA度量学习直方图均衡化图像处理模式识别
Keywords:
artificial intelligence pedestrain re-identification feature fusion down sampling XQDA measures learning histogram equalization image processing pattern recognition
分类号:
TP391
DOI:
10.3724/SP.J.1249.2019.04447
文献标志码:
A
摘要:
为解决实际监控场景中的行人重识别技术的智能应用,考虑到行人图像拍摄角度不断变化的情况,将颜色和纹理等特征进行融合,利用部分局部块提取图像特征;针对行人轮廓不清晰,提出在纹理特征提取前实现直方图均衡化的方法;通过对图像进行两次下采样,使算法具有更好的比例尺度不变性.与现有的局部最大概率(local maximal occurrence, LOMO)特征与交叉二次判别分析(cross-view quadratic discriminant analysis, XQDA)方法结合的重识别方法进行对比,结果表明,在数据集VIPeR、PKU-Reid和i-LIDS-VID上重识别率rank1分别提高了0.28%、1.75%和0.20%,证明采用最小均衡化后的行人重识别率得到了提升.
Abstract:
In order to solve the intelligent application of pedestrain re-identification technology in the actual monitoring scenes, the color and texture features are fused and some local blocks are used to extract image features by considering the changing shooting angleof pedestrian image. A method of histogram equalization before texture feature extraction is proposed to solve the problem of unclear pedestrian contour. By downsampling the image twice, the algorithm hasbetter scale invariance. Compared with the existing re-identification method of cross-view quadratic discriminant analysis (XQDA)combined with local maximal occurrence (LOMO) characteristics, the experimental results show that the corresponding re-identification rate rank1 on the datasets of VIPeR, PUK-Reid and i-LIDS-VID is improved by 0.28%, 1.75% and 0.20%, respectively, whichproves that the recognition rate of pedestrain re-identification with minimum equalization is improved.

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更新日期/Last Update: 2019-07-04