您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (5): 91-97.doi: 10.6040/j.issn.1672-3961.0.2018.347

• 机器学习与数据挖掘 • 上一篇    下一篇

基于奇异值分解行人对齐网络的行人重识别

张继(),金翠,王洪元*(),陈首兵   

  1. 常州大学信息科学与工程学院, 江苏 常州 213164
  • 收稿日期:2018-08-14 出版日期:2019-10-20 发布日期:2019-10-18
  • 通讯作者: 王洪元 E-mail:27106976@qq.com;hywang@cczu.edu.cn
  • 作者简介:张继(1981—),男,江苏常州人,讲师,硕士研究生,主要研究方向为计算机视觉.E-mail: 27106976@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61572085);国家自然科学基金资助项目(61806026);国家自然科学基金资助项目(61806026)

Pedestrian recognition based on singular value decomposition pedestrian alignment network

Ji ZHANG(),Cui JIN,Hongyuan WANG*(),Shoubing CHEN   

  1. School of Information Science and Engineering, Changzhou University, Changzhou 213164, Jiangsu, China
  • Received:2018-08-14 Online:2019-10-20 Published:2019-10-18
  • Contact: Hongyuan WANG E-mail:27106976@qq.com;hywang@cczu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61572085);国家自然科学基金资助项目(61806026);国家自然科学基金资助项目(61806026)

摘要:

为解决行人重识别的训练数据集中自动检测出的行人图像背景过大和行人部分缺失的错位现象问题,使用空间变换网络层对图像错位进行处理。为优化整个网络的深度学习过程,提高图像检索能力,增加网络特征层,使用奇异值向量分解方法对其进行处理。将行人对齐网络和奇异向量分解相结合,构造奇异值分解行人对齐网络,既可解决图像错位问题,又提高图像特征的相似性度量的效果。在Market1501、CUHK03和DukeMTMC-reID数据集上进行试验,并与行人对齐网络和其他深度学习与非深度学习的行人重识别方法进行比较,试验结果中整个网络的平均检索精度和行人图像第一次匹配正确的概率平均达到了65%和80%左右,这表明奇异值分解行人对齐网络可以提高对行人匹配的效果。

关键词: 行人重识别, 奇异向量分解, 行人对齐, 深度学习, 空间变换网络

Abstract:

In order to solve the problem that the background of pedestrian image was too large and the part of pedestrian was missing in the training data set of pedestrian recognition, the spatial transformation network layer was used to process the image dislocation. In order to optimize the deep learning process of the whole network and improve the image retrieval ability, a feature layer was added to the network, and singular value vector decomposition was used to process it. By combining the pedestrian alignment network with the singular vector decomposition and constructing the singular value decomposition pedestrian alignment network, the image dislocation problem could be solved and the effect of similarity measurement of image features could be improved. Experiments were conducted on Market1501, CUHK03 and DukeMTMC-reID datasets, and compared with pedestrian alignment network and other pedestrian re-recognition methods of deep learning and non-deep learning. In the experimental results, the values of rank-1 and mAP mean average precision reached 80% and 65% on average, which indicated that singular value decomposition of pedestrian alignment network had certain benefits on pedestrian matching effect.

Key words: pedestrian recognition, singular vector decomposition, pedestrian alignment, deep learning, spatial transformation network

中图分类号: 

  • TP391

图1

奇异值分解行人对齐网络框架图"

表1

不同数据集中rank-1和mAP的试验结果"

网络 方法 Market1501数据集 DukeMTMC-reID数据集 CUHK03(labeled)数据集 CUHK03(detected)数据集
rank-1/% mAP/% rank-1/% mAP/% rank-1/% mAP/% rank-1/% mAP/%
第1条分支 PAN 80.17 59.14 65.2 44.99 31.14 29.80 30.50 29.04
ours 81.38 62.02 66.74 45.96 79.19 78.82 77.06 80.29
第3条分支 PAN 79.01 58.27 69.36 47.14 35.29 32.90 34.14 31.71
ours 80.17 60.85 69.07 48.86 78.55 79.12 78.68 81.41
整个网络 PAN 82.81 63.35 68.36 51.51 36.86 35.03 36.29 34.05
ours 83.41 65.09 75.54 52.20 79.63 80.45 78.35 81.22

表2

SVDNet和奇异值分解行人对齐网络rank-1的试验结果比较"

%
方法 Market1501 DuckMTMC-reID CUHK03(labeled) CUHK03(detected)
SVDNet 82.3 80.05 81.80 76.70
ours 83.41 75.54 79.63 78.35

表3

PAN和奇异值分解行人对齐网络rank-1的试验结果比较"

%
方法 Market1501 DuckMTMC-reID CUHK03(labeled) CUHK03(detected)
PAN 82.81 80.05 36.86 36.29
ours 83.41 75.54 79.63 78.35

表4

不同行人重识别方法在Market1501和CUHK03数据集上rank-1和mAP的试验结果比较"

%
方法 Market1501 CUHK03
rank-1 mAP rank-1 mAP
LOMO+XQDA 43.80 22.20 44.60 51.50
Siamese LSTM 61.60 35.30 57.30 46.30
Gated SCNN 65.90 39.60 61.80 51.30
DLCE 79.50 59.90 79.63 80.45
ours 83.41 65.09 79.52 81.34

表5

不同行人重识别方法在DukeMTMC-reID数据集上rank-1和mAP的试验结果比较"

%
方法 rank-1 mAP
Bow+kissme 25.1 12.2
LOMO+XQDA 30.8 17
Baseline 65.5 44.1
GAN 67.7 47.1
ours 75.54 52.2
1 ZHENG Liang, SHEN Liyue, TIAN Lu, et al. Scalable person re-identification: a benchmark[C]//IEEE International Conference on Computer Vision. Santiago, Chile: IEEE Computer Society, 2015: 1116-1124.
2 WANG Hongyuan , DING Zongyuan , ZHANG Ji , et al. Person reidentification by semisupervised dictionary rectification learning with retraining module[J]. Journal of Electronic Imaging, 2018, 27 (4): 043043-1- 9.
3 NI Tongguang , DING Zongyuan , CHEN Fuhua , et al. Relative distance metric leaning based on clustering centralization and projection vectors learning for person re-identification[J]. IEEE Access, 2018, 6 (1): 11405- 11411.
4 丁宗元, 王洪元, 陈付华, 等. 基于距离中心化与投影向量学习的行人重识别[J]. 计算机研究与发展, 2017, 54 (8): 1785- 1794.
DING Zongyuan , WANG Hongyuan , CHEN Fuhua , et al. Pedestrian weight recognition based on distance centralization and projection vector learning[J]. Computer Research and Development, 2017, 54 (8): 1785- 1794.
5 WANG Hongyuan, DING Zongyuan, NI Tongguang, et al. KL divergence based person re-identification using multivariate Gaussian distributions[C]//Proceedings of the 2017 4th Asian Conference on Pattern Recognition. Nanjing, China: Sponsorship, 2017: 417-422.
6 张文文, 王洪元, 万建武, 等. 基于稀疏学习的行人重识别算法[J]. 数据采集与处理, 2018, 33 (5): 855- 864.
ZHANG Wenwen , WANG Hongyuan , WAN Jianwu , et al. Pedestrian weight recognition algorithm based on sparse learning[J]. Data Acquisition and Processing, 2018, 33 (5): 855- 864.
7 NI Tongguang , GU Xiaoqing , WANG Honyuan , et al. Discirminative deep trasfer metric learning for cross-scenario person re-identification[J]. Journal of Electronic Imaging, 2018, 27 (4): 043026- 1.
8 陈首兵, 王洪元, 金翠, 等. 基于孪生网络和重排序的行人重识别[J]. 计算机应用, 2018, 38 (11): 3161- 3166.
doi: 10.11772/j.issn.1001-9081.2018041223
CHEN Shoubing , WANG Hongyuan , JIN Cui , et al. Pedestrian weight recognition based on twin networks and reordering[J]. Computer Applications, 2018, 38 (11): 3161- 3166.
doi: 10.11772/j.issn.1001-9081.2018041223
9 金翠, 王洪元, 陈首兵. 基于随机擦除行人对齐网络的行人重识别方法[J]. 山东大学学报(工学版), 2018, 48 (6): 67- 73.
JIN Cui , WANG Hongyuan , CHEN Shoubing . Pedestrian weight recognition method based on random erasure pedestrian alignment network[J]. Journal of Shandong University(Engineering Science Edition), 2018, 48 (6): 67- 73.
10 DENTON E, ZAREMBA W, BRUNA J, et al. Exploiting linear structure within convolutional networks for efficient evaluation[C]//Proceedings of the Conference and Workshop on Neural Information Processing Systems. Montréal, Canada: IEEE Computer Society, 2014: 1269-1277.
11 ZHAO Liming, LI Xi, WANG Jingdong, et al. Deeply-learned part-aligned representations for person re-identification[C]//Proceedings of the International Conference on Computer Vision. Venice, Italy: IEEE Computer Society, 2017: 3219-3228.
12 ZHENG Zhedong, ZHENG Liang, YANG Yi. Pedestrian alignment network for large-scale person re-identification[J/OL].[2018-12-20]. https://arxiv.org/pdf/1707.00408.pdf.
13 SUN Yifan, ZHENG Liang, DENG Weijian. SVDnet for pedestrian retrieval[C]//Proceedings of the International Conference on Computer Vision. Venice, Italy: IEEE Computer Society, 2017: 3820-3828.
14 SU Chi, ZHANG Shiliang, XING Junliang, et al. Deep attributes driven multi-camera person re-identification[C]//Proceedings of the European Conference on Computer Vision. Amsterdam, the Netherlands: IEEE Computer Society, 2016: 475-491.
15 SARFRAZ M Saquib, SCHUMANN Arne, EBERLE Andreas, et al. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking[C]//Proceedings of the IEEE Computer Vision and Pattern Recognition(CVPR). Salt Lake City, Utah: IEEE Computer Society, 2018: 420-429.
16 GUO Yiluan, CHEUNG Ngai-man.. Efficient and Deep person re-identification using multi-level similarity[C]//Proceedings of the IEEE Computer Vision and Pattern Recognition(CVPR). Salt Lake City, USA: IEEE Computer Society, 2018: 2335-2344.
17 HE Lingxiao, LIANG Jian, LI Haiqing, et al. Deep spatial feature reconstruction for partial person re-identification: alignment-free approach[C]//Proceedings of the IEEE Computer Vision and Pattern Recognition(CVPR). Salt Lake City, Utah: IEEE Computer Society, 2018: 7073-7082.
18 ZHONG Zhun, ZHENG Liang, CAO Donglin, et al. Re-ranking person re-identification with k-reciprocal encoding[C]//Proceedings of the IEEE Computer Vision and Pattern Recognition(CVPR). Honolulu, Hawaii: IEEE Computer Society, 2017: 3652-3661.
19 LIAO Shengcai, HU Yang, ZHU Xiangyu, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the IEEE Computer Vision and Pattern Recognition(CVPR). Boston, Massachusetts: IEEE Computer Society, 2015: 2197-2206.
20 VARIOR Rahulrama, SHUAI Bing, LU Jiwen, et al. A siamese long short-term memory architecture for human reidentification[C]//Proceedings of the European Conference on Computer Vision, . Amsterdam, the Netherlands: IEEE Computer Society, 2016: 135-153.
21 VARIOR Rahulrama, HALOI Mrinal, WANG Gang. Gated siamese convolutional neural network architecture for human reidentification[C]//Proceedings of the European Conference on Computer Vision. Amsterdam, the Netherlands: IEEE Computer Society, 2016: 791-808.
[1] 万鹏. 基于F-PointNet的3D点云数据目标检测[J]. 山东大学学报 (工学版), 2019, 49(5): 98-104.
[2] 梁志祥,刘晓明,牟颖,刘玉田. 基于深度学习的新能源爬坡事件预测方法[J]. 山东大学学报 (工学版), 2019, 49(5): 24-28.
[3] 刘玉田,孙润稼,王洪涛,顾雪平. 人工智能在电力系统恢复中的应用综述[J]. 山东大学学报 (工学版), 2019, 49(5): 1-8.
[4] 李力钊,蔡国永,潘角. 基于C-GRU的微博谣言事件检测方法[J]. 山东大学学报 (工学版), 2019, 49(2): 102-106, 115.
[5] 侯霄雄,许新征,朱炯,郭燕燕. 基于AlexNet和集成分类器的乳腺癌计算机辅助诊断方法[J]. 山东大学学报 (工学版), 2019, 49(2): 74-79.
[6] 张成彬,赵慧,曹宗钰. 基于深度学习的车身网络KWP2000协议漏洞挖掘[J]. 山东大学学报 (工学版), 2019, 49(2): 17-22.
[7] 金翠,王洪元,陈首兵. 基于随机擦除行人对齐网络的行人重识别方法[J]. 山东大学学报 (工学版), 2018, 48(6): 67-73.
[8] 谢志峰,吴佳萍,马利庄. 基于卷积神经网络的中文财经新闻分类方法[J]. 山东大学学报(工学版), 2018, 48(3): 34-39.
[9] 唐乐爽,田国会,黄彬. 一种基于DSmT推理的物品融合识别算法[J]. 山东大学学报(工学版), 2018, 48(1): 50-56.
[10] 周福娜,高育林,王佳瑜,文成林. 基于深度学习的缓变故障早期诊断及寿命预测[J]. 山东大学学报(工学版), 2017, 47(5): 30-37.
[11] 何正义,曾宪华,曲省卫,吴治龙. 基于集成深度学习的时间序列预测模型[J]. 山东大学学报(工学版), 2016, 46(6): 40-47.
[12] 郑毅, 朱成璋. 基于深度信念网络的PM2.5预测[J]. 山东大学学报(工学版), 2014, 44(6): 19-25.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 陈瑞,李红伟,田靖. 磁极数对径向磁轴承承载力的影响[J]. 山东大学学报(工学版), 2018, 48(2): 81 -85 .
[2] 刘文亮,朱维红,陈涤,张泓泉. 基于雷达图像的运动目标形态检测及跟踪技术[J]. 山东大学学报(工学版), 2010, 40(3): 31 -36 .
[3] Yue Khing Toh1 , XIAO Wendong2 , XIE Lihua1 . 基于无线传感器网络的分散目标跟踪:实际测试平台的开发应用(英文)[J]. 山东大学学报(工学版), 2009, 39(1): 50 -56 .
[4] 刘新1 ,宋思利1 ,王新洪2 . 石墨配比对钨极氩弧熔敷层TiC增强相含量及分布形态的影响[J]. 山东大学学报(工学版), 2009, 39(2): 98 -100 .
[5] 田芳1,张颖欣2,张礼3,侯秀萍3,裘南畹3. 新型金属氧化物薄膜气敏元件基材料的开发[J]. 山东大学学报(工学版), 2009, 39(2): 104 -107 .
[6] 赵延风1,2, 王正中1,2 ,芦琴1,祝晗英3 . 梯形明渠水跃共轭水深的直接计算方法[J]. 山东大学学报(工学版), 2009, 39(2): 131 -136 .
[7] 梁京芸,王明刚,柴家前,刘永庆 . 1.6-二-(N5-取代苯基-N1-二胍)己烷盐酸盐的合成和体外抗菌活性[J]. 山东大学学报(工学版), 2008, 38(3): 104 -107 .
[8] 宋青,李晓磊,张承进 . 基于瓶颈分析的邮政速递网络的优化[J]. 山东大学学报(工学版), 2007, 37(5): 29 -33 .
[9] 孟健, 李贻斌, 李彬. 四足机器人跳跃步态控制方法[J]. 山东大学学报(工学版), 2015, 45(3): 28 -34 .
[10] 何东之, 张吉沣, 赵鹏飞. 不确定性传播算法的MapReduce并行化实现[J]. 山东大学学报(工学版), 0, (): 22 -28 .