融合直接度量和间接度量的行人再识别

doi:10.16451/j.cnki.issn1003-6059.201802008

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Abstract The metric algorithm for person re-identification to compute similarity of the image pairs is mostly based on the discriminant information of themselves rather than the discriminant information of other images related to them. Therefore, a metric method is proposed to fuse direct metric and indirect metric by weighing them. Firstly, the local maximal occurrence feature and salient color name feature of the images are extracted, and two features are fused as the final feature of the image. Then, the direct similarity and the indirect similarity of two images are calculated respectively. Finally, the sequence sorting method is further proposed to obtain the weights by training the database samples, and thus the final similarity of two images is acquired. The experimental results on Market-1501 database and CUHK03 database show that the recognition ability of fusion metric is obviously higher than that of the single metric.

Key words： Pedestrian Re-identification Direct Metric Indirect Metric Metric Fusion

Received: 17 July 2017

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61175026), Natural Science Foundation of Zhejiang Province(No.LY17F030002), Zhejiang Provincial Public Welfare Technology Research Project(No.LGF18F020007)

About author:: JIANG Huihui, master student. Her research interests include computer vision and pattern recognition.ZHANG Rong, Ph.D., associate profe-ssor. Her research interests include digital forensics and information security.LI Xiaobao, master student. His research interests include computer vision and pattern recognition.GUO Lijun(Corresponding author), Ph.D., professor. His research interests include computer vision, pattern recognition, mobile internet and its application.

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Cite this article:

JIANG Huihui,ZHANG Rong,LI Xiaobao等. Pedestrian Re-identification Fusing Direct Metric and Indirect Metric[J]. , 2018, 31(2): 167-174.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201802008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I2/167

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