一种基于等距度量学习策略的行人重识别改进算法

周智恒; 刘楷怡; 黄俊楚; 陈增群

doi:10.11999/JEIT180336

一种基于等距度量学习策略的行人重识别改进算法

doi: 10.11999/JEIT180336

华南理工大学电子与信息学院广州 510000

基金项目: 国家自然科学基金(U1401252, 61871188)，国家重点研发计划(2018YFC0309400)，中央高校基本科研业务费专项资金(2017MS062)，广州市产学研协同创新重大专项(201604016133)

详细信息

作者简介:
周智恒：男，1977年生，教授，博士生导师，研究方向为模式识别与人工智能

刘楷怡：女，1994年生，硕士生，研究方向为图像处理与模式识别

黄俊楚：男，1994年生，博士生，研究方向为图像处理与模式识别

陈增群：男，1995年生，本科生，研究方向为图像处理与模式识别

通讯作者:
周智恒　zhouzh@scut.edu.cn

中图分类号: TP391.41
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- 被引次数: 0
出版历程
- 收稿日期: 2018-04-11
- 修回日期: 2018-09-13
- 网络出版日期: 2018-09-20
- 刊出日期: 2019-02-01

Improved Metric Learning Algorithm for Person Re-identification Based on Equidistance

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510000, China

Funds: The National Natural Science Foundation of China (U1401252，61871188), The National Key R&D Program of China (2018YFC0309400), The Fundamental Research Funds for the Central Universities SCUT (2017MS062), Guangzhou City Science and Technology Research Projects (201604016133)

摘要

摘要:
为了提高行人重识别距离度量MLAPG算法的鲁棒性，该文提出基于等距度量学习策略的行人重识别Equid-MLAPG算法。 MLAPG算法中正负样本对在映射空间的分布不均衡导致间距超参数受负样本对距离影响更大，因此该文设计的Equid-MLAPG算法要求正样本对映射成为变换空间中的一个点，即正样本对在变换空间中距离为零，使算法收敛时正负样本对距离分布不存在交叉部分。实验表明Equid-MLAPG算法能在常用的行人重识别数据集上取得良好的实验效果，具有更好的识别率和广泛的适用性。
- 行人重识别 /
- 等距度量 /
- MLAPG算法
Abstract:
In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm is proposed, which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
- Person re-identification /
- Equidistance /
- MLAPG algorithm

HTML全文

图 1 MLAPG算法中$\mu $取值和训练过程中所有样本对马氏距离均值对比示意图

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图 2 对数逻辑损失函数变化趋势

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图 3 在不同限制条件下正负样本对距离情况

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图 4 正负样本分布区域重叠示意图

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图 5 交换空间中样本分类情况

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图 6 VIPeR数据集上Equid-MLAPG算法与其他距离度量算法CMC曲线图

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图 7 CUHK01数据集上Equid-MLAPG算法与其他距离度量算法CMC曲线图

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表 1 CUHK03数据集上多种距离度量算法对比

算法	检测标注			人工标注
算法	第1匹配率(%)	第5匹配率(%)	第10匹配率(%)	第1匹配率(%)	第5匹配率(%)	第10匹配率(%)
XQDA	46.25	78.90	88.55	52.20	82.23	92.14
MLAPG	51.15	83.55	92.05	57.96	87.09	94.74
Nullspace	53.70	83.05	90.30	58.90	85.60	92.45
Equid-MLAPG	52.41	85.25	92.84	58.72	89.07	95.28

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表 2 Marlet1501，DukeMTMC-reID数据集上多种距离度量算法对比

算法	Market1501数据集		DukeMTMC-reID数据集
算法	第1匹配率(%)	平均准确率(%)	第1匹配率(%)	平均准确率(%)
XQDA	43.23	22.00	31.37	17.17
MLAPG	42.52	21.45	36.58	19.10
Nullspace	54.60	29.80	45.02	26.11
Equid-MLAPG	44.25	24.38	39.25	21.54

下载: 导出CSV

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