| HOU Wei(侯巍),HU Zhentao,LIU Xianxing,SHI Changsen.[J].高技术通讯(英文),2022,28(3):237~246 |
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| Feature mapping space and sample determination for person re-identification |
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| DOI：10.3772/j.issn.1006-6748.2022.03.002 |
| 中文关键词: |
| 英文关键词: person re-identification (Re-ID), mapping space, feature optimization, sample determination |
| 基金项目: |
| Author Name | Affiliation | | HOU Wei(侯巍) | (School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China) | | HU Zhentao | (School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China) | | LIU Xianxing | (School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China) | | SHI Changsen | (School of Artificial Intelligence, Henan University, Zhengzhou 450046, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Person re-identification (Re-ID) is integral to intelligent monitoring systems. However, due to the variability in viewing angles and illumination, it is easy to cause visual ambiguities, affecting the accuracy of person re-identification. An approach for person re-identification based on feature mapping space and sample determination is proposed. At first, a weight fusion model, including mean and maximum value of the horizontal occurrence in local features, is introduced into the mapping space to optimize local features. Then, the Gaussian distribution model with hierarchical mean and covariance of pixel features is introduced to enhance feature expression. Finally, considering the influence of the size of samples on metric learning performance, the appropriate metric learning is selected by sample determination method to further improve the performance of person re-identification. Experimental results on the VIPeR, PRID450S and CUHK01 datasets demonstrate that the proposed method is better than the traditional methods. |
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