嵌套池化三元组卷积神经网络的行人再识别

王林; 杨乐

Chinese

您当前的位置：

首页 >

文章列表页 >

嵌套池化三元组卷积神经网络的行人再识别

更新时间：2026-09-04

- 嵌套池化三元组卷积神经网络的行人再识别
- Pedestrian re-identification of nested pooling triple convolutional neural networks
- 微电子学与计算机 2019年36卷第9期页码：73-78
- 作者机构：
  
  西安理工大学机构自动化与信息工程学院,陕西,西安,710048
- 作者简介：
- 基金信息：
  
  陕西省科技计划重点项目 2017ZDCXL-GY-05-03
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
移动端阅览
王林, 杨乐. 嵌套池化三元组卷积神经网络的行人再识别[J]. 微电子学与计算机, 2019, 36(9): 73-78.

WANG Lin, YANG Le. Pedestrian re-identification of nested pooling triple convolutional neural networks[J]. 2019, 36(9): 73-78.
王林, 杨乐. 嵌套池化三元组卷积神经网络的行人再识别[J]. 微电子学与计算机, 2019, 36(9): 73-78. DOI：

WANG Lin, YANG Le. Pedestrian re-identification of nested pooling triple convolutional neural networks[J]. 2019, 36(9): 73-78. DOI：

摘要

针对行人再识别易受遮挡

光照、视角等非理想条件变化的影响

提出一种嵌套池化三元组卷积神经网络.它在均方根池化后依次添加平均池化和最大池化提取全局特征

并通过最短路径损失自动对齐局部特征.然后采用改进型Log-logistic函数代替传统三元组损失函数训练网络

得到与局部特征联合优化的全局特征.在Market-1501、CUHK03和VIPeR数据集上的识别率都比基于传统方法的提高了5%以上.实验结果表明

本文提出的嵌套池化三元组卷积神经网络

能有效解决非理想自然条件下存在的部分遮挡、分辨率低和旋转变化等问题

同时具有良好的泛化能力和适用范围.

Abstract

A nested pooling tri-tuple convolution neural network is proposed for pedestrian re-identification

which is vulnerable to occlusion

illumination

viewing angle and other non-ideal conditions. It adds the average pooling and maximum pooling to extract global features in turn after the root mean square pooling

and automatically aligns the local features with the shortest path loss. Then the improved Log-logistic function is used instead of the traditional triple loss function to train the network

and get global features jointly optimized with local features. The recognition rates on the Market-1501

CUHK03

and VIPeR datasets are both more than 6% higher than those based on traditional methods. The experimental results show that nested pooling tri-tuple convolutional neural network proposed in this paper can effectively solve the problems of partial occlusion

low resolution and rotation change under non-ideal natural conditions

and has good generalization ability and scope of application.

关键词

Keywords

references

SONG Z, CAI X, CHEN Y, et al. Deep convolutional neural networks with adaptive spatial feature for person re-identification[C]// Advanced Information Technology, Electronic and Automation Control Conference. Chongqing, China, IEEE, 2017: 2020-2023.

ZHANG X, LUO H, FAN X, et al. Alignedreid: Surpassing human-level performance in person re-identification[J]. arXiv preprint arXiv: 1711.08184, 2017, 31(2): 1-10.

LOU Y, BAI Y, LIN J, et al.Compact deep invariant descriptors for video Rretrieval[C]//Data Compression Conference[s.l.]. IEEE, 2017: 420-429.

CARR S B P. Deep spatial pyramid for person re-identification[C]// IEEE International Conference on Advanced Video and Signal Based Surveillance. Lecce, Italy, IEEE, 2017: 1-6.

MIRMAHBOUB B, MEKHAFI M L, MURINO V, et al. Person re-identification by order-induced metric fusion[J]. Neurocomputing, 2018(275):667-676. DOI: 667

LIAO S, HU Y, ZHU X, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Computer Vision and Pattern Recognition. Boston, Massachusetts, USA, IEEE, 2015: 2197-2206.

BаK S, CARR P. Deep deformable patch metric learning for person re-identification[J]. IEEE Transactions on Circuits % Systems for Video Technology, 2017(99):1-3. DOI: 1

PAISITKRIANGKRAI S, SHEN C, HENGEL A V D.Learning to rank in person re-identification with metric ensembles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA, USA: IEEE, 2015: 1846-1855.

HERMANS A, BEYER L, LEIBE B. In defense of the triplet loss for person re-identification[J].arXiv preprint arXiv: 1703.07737, 2017, 4(21): 1-17.

朱建清, 曾焕强, 杜永兆, 等.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: 1012

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

超低功耗实时时钟电路的设计

V型沟槽SiC中子探测器的蒙特卡洛分析

基于区块链的AI生成内容溯源框架设计研究

基于反向体偏置的高增益稳定性高温运放设计

大模型发展综述：从BERT到Deepseek的技术演进