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Multi-level feature fusion model-based real-time person re-identification for forensics

机译：基于多层特征融合模型的取证人员实时识别

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摘要

Person forensics aims to retrieve the specified person across non-overlapping cameras. It is difficult owing to the appearance variations caused by occlusion, human pose change, background clutter, illumination variation, etc. In this scenario, current models face great challenges in extracting effective features. Recent deep learning models mainly focus on extracting representative deep features to cope with appearance variations, while handcrafted features are not fully explored. In this paper, a multi-level feature fusion model (MFFM) is designed to combine both deep features and handcrafted features in real time. MFFM is first utilized to describe person appearance. Then, local binary pattern (LBP) and histogram of oriented gradient (HOG) are extracted to cope with geometric change and illumination variance. Using LBP and HOG, 11.89% on the CUHK03, 15.30% on the Market-1501 and 8.25% on the VIPeR top-1 recognition accuracy improvement for the proposed method are achieved with only 9.66%, 4.90%, and 7.59% extra processing time. Experimental results indicate MFFM can achieve the best performance compared to the state-of-the-art models on the Market1501, CUHK03, and VIPeR datasets.
机译：人员取证的目的是在不重叠的摄像机之间检索指定的人员。由于遮挡，人体姿势变化，背景杂波，照明变化等导致的外观变化，因此很难。在这种情况下，当前模型在提取有效特征方面面临巨大挑战。近期的深度学习模型主要集中在提取代表性的深度特征以应对外观变化，而手工特征并未得到充分探索。在本文中，设计了一个多级特征融合模型（MFFM）以实时结合深度特征和手工特征。 MFFM首先用于描述人的外表。然后，提取局部二进制图案（LBP）和定向梯度直方图（HOG）以应对几何变化和照明差异。使用LBP和HOG，仅需9.66％，4.90％和7.59％的额外处理时间，即可实现CUHK03的11.89％，Market-1501的15.30％和VIPeR top-1识别精度的8.25％的提高。 。实验结果表明，与Market1501，CUHK03和VIPeR数据集上的最新模型相比，MFFM可以实现最佳性能。

著录项

  • 来源
    《Journal of Real-Time Image Processing》 |2020年第1期|73-81|共9页
  • 作者

    Wang Shiqin; Xu Xin; Liu Lei; Tian Jing;

  • 作者单位

    Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China|Wuhan Univ Sci & Technol Artificial Intelligence Inst Wuhan 430065 Peoples R China;

    Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China|Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China|Wuhan Univ Sci & Technol Artificial Intelligence Inst Wuhan 430065 Peoples R China;

    Natl Univ Singapore Inst Syst Sci Singapore 119615 Singapore;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    LBP; HOG; Multi-levels; Fusion;

    机译：LBP;猪多层次融合;

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