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Deep Camera-Aware Metric Learning for Person Reidentification
Wireless Communications and Mobile Computing ( IF 2.146 ) Pub Date : 2021-01-06 , DOI: 10.1155/2021/8859088
Wei Liu 1 , Ping Liang 1, 2 , Lei Liu 1 , Zhiqiang Hao 3 , Xin Xu 1
Affiliation  

Person reidentification (re-id) suffers from a challenging issue due to the significant inconsistency of the camera network, including position, view, and brands. In this paper, we propose a deep camera-aware metric learning (DCAML) model, where images on the identity-level spaces are further projected into different camera-level subspaces, which can explore the inherent relationship between identity and camera. Furthermore, we exploit dynamic training strategy to jointly multiple metrics for identity-camera relationship learning and thus consumedly elevating the retrieval accuracy. Extensive experiments on the three public datasets demonstrated that our method performs competitive results compared to the state-of-the-art person re-id methods.

中文翻译:

用于人员识别的深度相机感知度量学习

由于摄像机网络(包括位置,视图和品牌)的显着不一致,因此人员重新识别(re-id)面临着一个具有挑战性的问题。在本文中,我们提出了一种深度相机感知度量学习(DCAML)模型,该模型将身份级别空间上的图像进一步投影到不同的相机级别子空间中,从而可以探索身份与相机之间的固有关系。此外,我们利用动态训练策略将多个度量联合用于身份-摄像机关系学习,从而极大地提高了检索准确性。在三个公共数据集上进行的大量实验表明,与最新的人员再利用方法相比,我们的方法具有竞争优势。
更新日期:2021-01-06
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