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基于新型三元卷积神经网络的行人再辨识算法

朱建清 曾焕强 杜永兆 雷震 郑力新 蔡灿辉

朱建清, 曾焕强, 杜永兆, 雷震, 郑力新, 蔡灿辉. 基于新型三元卷积神经网络的行人再辨识算法[J]. 电子与信息学报, 2018, 40(4): 1012-1016. doi: 10.11999/JEIT170803
引用本文: 朱建清, 曾焕强, 杜永兆, 雷震, 郑力新, 蔡灿辉. 基于新型三元卷积神经网络的行人再辨识算法[J]. 电子与信息学报, 2018, 40(4): 1012-1016. doi: 10.11999/JEIT170803
ZHU Jianqing, ZENG Huanqiang, DU Yongzhao, LEI Zhen, ZHENG Lixin, CAI Canhui. Person Re-identification Based on Novel Triplet Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(4): 1012-1016. doi: 10.11999/JEIT170803
Citation: ZHU Jianqing, ZENG Huanqiang, DU Yongzhao, LEI Zhen, ZHENG Lixin, CAI Canhui. Person Re-identification Based on Novel Triplet Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(4): 1012-1016. doi: 10.11999/JEIT170803

基于新型三元卷积神经网络的行人再辨识算法

doi: 10.11999/JEIT170803
基金项目: 

国家自然科学基金(61602191, 61401167, 61473291, 61605048, 61372107),福建省自然科学基金(2016J01308),厦门市科技计划项目(3502Z20173045),华侨大学中青年教师科技创新资助计划(ZQN-PY418, ZQN-YX403, ZQN-PY518),华侨大学科研基金资助项目(16BS108)

Person Re-identification Based on Novel Triplet Convolutional Neural Network

Funds: 

The National Natural Science Foundation of China (61602191, 61401167, 61473291, 61605048, 61372107), The Natural Science Foundation of Fujian Province (2016J01308), The Scientific and Technology Funds of Xiamen (3502Z20173045), The Promotion Program for Young and Middle Aged Teacher in Science and Technology Research of Huaqiao University (ZQN-PY418, ZQN-YX403, ZQN-PY518), The Scientific Research Funds of Huaqiao University (16BS108)

  • 摘要: 基于三元卷积神经网络的行人再辨识算法多数采用欧式距离度量行人之间的相似度,并配合铰链(hinge)损失函数进行卷积神经网络的训练。然而,这种作法存在两个不足:欧式距离作为行人相似度,鉴别力不够强;铰链损失函数的间隔(Margin)参数设定依赖于人工预先设定且在训练过程中无法自适应调整。为此,针对上述两个不足进行改进,该文提出一种基于新型三元卷积神经网络的行人再辨识算法,以提高行人再辨识的准确率。首先,提出一种归一化混合度量函数取代传统的度量方法进行行人相似度计算,提高了行人相似度度量的鉴别力;其次,提出采用Log-logistic函数代替铰链函数,无需人工设定间隔参数,改进了特征与度量函数的联合优化效果。实验结果表明,所提出的算法在Auto Detected CUHK03 和VIPeR两个数据库上的准确率均获得显著的提升,验证了所提出算法的优越性。
  • GRAY Douglas and TAO Hai. Viewpoint invariant pedestrian recognition with an ensemble of localized features [C]. European Conference on Computer Vision, Marseille- France in Palais des Congrs Parc Chanot, 2008: 262-275.
    FARENZENA M, BAZZANI L, PERINA A, et al. Person re-identification by symmetry-driven accumulation of local features[C]. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2360-2367.
    LIAO Shengcai and LI Stan Z. Efficient PSD constrained asymmetric metric learning for person re-identification[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3685-3693.
    MATSUKAWA Tetsu, OKABE Takahiro, SUZUKI Einoshin, et al. Hierarchical gaussian descriptor for person re- identification[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1363-1372.
    CHEN Dapeng, YUAN Zejian, CHEN Badong, et al. Similarity learning with spatial constraints for person re-identification[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1268-1277.
    YANG Xun, WANG Meng, HONG Richang, et al. Enhancing person re-identification in a self-trained subspace[OL]. https://arxiv.org/pdf/1704.06020, 2017.
    YANG Yang, WEN Longyin, LYU Siwei, et al. Unsupervised learning of multi-level descriptors for person re-identification [C]. AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 2017: 4306-4312.
    WU Shangxuan, CHEN Ying Cong, LI Xiang, et al. An enhanced deep feature representation for person re-identification[C]. IEEE Winter Conference on Applications of Computer Vision, Lake Placid, NewYork, USA, 2016: 1-8.
    XIAO Tong, LI Hongsheng, OUYANG Wanli, et al. Learning deep feature representations with domain guided dropout for person re-identification[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 1249-1258.
    LI Wei, ZHAO Rui, XIAO Tong, et al. Deepreid: Deep filter pairing neural network for person re-identification [C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, Ohio, USA, 2014: 152-159.
    YI Dong, LEI Zhen, LIAO Shengcai, et al. Deep metric learning for person re-identification[C]. International Conference on Pattern Recognition, Stockholm, Sweden, 2014: 34-39.
    VARIOR Rahul Rama, HALOI Mrinal, and WANG Gang. Gated siamese convolutional neural network architecture for human re-identification[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 791-808.
    WU Lin, WANG Yang, LI Xue, et al. What-and-where to match: deep spatially multiplicative integration networks for person re-identification[OL]. https://arxiv.org/pdf/1707. 07074, 2017.
    ZHU Jianqing, ZENG Huanqiang, LIAO Shengcai, et al. Deep hybrid similarity learning for person re-identification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, (99): 1. doi: 10.1109/TCSVT.2017. 2734740.
    CHEN S Z, GUO C C, and LAI J. Deep ranking for person re-identification via joint representation learning[J]. IEEE Transactions on Image Processing, 2016, 25(5): 2353-2367. doi: 10.1109/TIP.2016.2545929.
    ZHAO Liming, LI Xi, WANG Jingdong, et al. Deeply- learned part-aligned representations for person re- identification[OL]. https://arxiv.org/pdf/1707.07256, 2017.
    LIU H, FENG J, QI M, et al. End-to-end comparative attention networks for person re-identification[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3492-3506.
    IOFFE Sergey and SZEGEDY Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. International Conference on Machine Learning, Lille, France, 2015: 448-456.
    KRIZHEVSKY Alex, SUTSKEVER Ilya, and HINTON Geoffrey E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012: 1097-1105.
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出版历程
  • 收稿日期:  2017-08-08
  • 修回日期:  2018-01-10
  • 刊出日期:  2018-04-19

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