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基于多层级特征的行人重识别

Person Re-Identification Based on Multi-Layer Feature

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摘要

针对现有的行人重识别方法提取行人特征过程中存在因信息缺失导致鲁棒性和判别力较差的问题,提出了一种基于残差神经网络提取行人图像多层级特征的方法。该方法在训练阶段使用残差网络分别在4个卷积残差模块之后提取阶段特征,以此来弥补信息丢失,使用三元组损失函数对每个特征向量进行监督训练。在相似性度量阶段,针对4个特征向量分别计算特征相似度,使用映射函数进行求和,并对求和结果进行相似度匹配。将该方法在Market-1501和DukeMTMC-ReID数据集上进行仿真,首中准确率(Rank-1)分别达到了91.7%和84.9%,平均准确率(mAP)分别达到了86.8%和80.7%。结果表明所提方法提取的多层级特征具有较好的鲁棒性和判别力,提高了行人重识别的准确度。

Abstract

To address the issue that existing person re-identification (Re-ID) algorithms have low robustness and discriminative capability when extracting pedestrian features with information loss, a novel Re-ID algorithm based on residual neural network is proposed for extracting multi-layer features of pedestrian images. During training phase, the residual network is used to extract the phase features after the four convolutional residual modules, to compensate for the information loss. And the triple loss function is used to supervise training of each feature vector. During the similarity measurement phase, the feature similarity is calculated according to the four feature vectors, the similarity of each stage is calculated by the summation of mapping function, and then the result of the summation is used to perform similarity matching. During the experiment, we validate the proposed algorithm on the Market-1501 and DukeMTMC-ReID datasets. The accuracy (Rank-1) of our algorithm reaches 91.7% and 84.9% and mean average precision (mAP) reaches 86.8% and 80.7%, respectively. Experimental results show that the multi-layer features extracted by our algorithm have considerable robustness and discriminative capability, which improves the accuracy of Re-ID.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.4

DOI:10.3788/LOP57.081503

所属栏目:机器视觉

基金项目:国家重点研发计划;

收稿日期:2019-06-26

修改稿日期:2019-09-20

网络出版日期:2020-04-01

作者单位    点击查看

刘可文:武汉理工大学信息工程学院, 湖北 武汉 430070武汉理工大学宽带无线通信和传感器网络湖北省重点实验室, 湖北 武汉 430070
房攀攀:武汉理工大学信息工程学院, 湖北 武汉 430070武汉理工大学宽带无线通信和传感器网络湖北省重点实验室, 湖北 武汉 430070
熊红霞:武汉理工大学土木工程与建筑学院, 湖北 武汉 430070
刘朝阳:中国科学院武汉物理与数学研究所波谱与原子分子物理国家重点实验室, 湖北 武汉 430071
马圆:武汉理工大学信息工程学院, 湖北 武汉 430070武汉理工大学宽带无线通信和传感器网络湖北省重点实验室, 湖北 武汉 430070
李小军:武汉理工大学信息工程学院, 湖北 武汉 430070武汉理工大学宽带无线通信和传感器网络湖北省重点实验室, 湖北 武汉 430070
陈亚雷:武汉理工大学信息工程学院, 湖北 武汉 430070武汉理工大学宽带无线通信和传感器网络湖北省重点实验室, 湖北 武汉 430070

联系人作者:熊红霞(xionghongxia@whut.edu.cn)

备注:国家重点研发计划;

【1】Liao S C, Hu Y, Zhu X Y, et al. Person re-identification by Local Maximal Occurrence representation and metric learning . [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE. 2015, 2197-2206.

【2】Sun Y. Research on person reidentification technology based on deep feature in surveillance video [D]. Xiamen: Xiamen University. 2017.
孙妍. 基于深度特征的监控视频下的行人重识别技术研究 [D]. 厦门: 厦门大学. 2017.

【3】Zhu X B, Che J. Person re-identification algorithm based on feature fusion and subspace learning [J]. Laser & Optoelectronics Progress. 2019, 56(2): 021503.
朱小波, 车进. 基于特征融合与子空间学习的行人重识别算法 [J]. 激光与光电子学进展. 2019, 56(2): 021503.

【4】Dalal N, Triggs B. Histograms of oriented gradients for human detection . [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR''''05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE. 2005, 8588935.

【5】Sun X W, Xu Q S, Cai Y, et al. Sea sky line detection based on edge phase encoding in complicated background [J]. Acta Optica Sinica. 2017, 37(11): 1110002.
孙熊伟, 徐青山, 蔡熠, 等. 基于边缘相位编码的复杂背景下海天线检测 [J]. 光学学报. 2017, 37(11): 1110002.

【6】Cheng D, Gong Y H, Zhou S P, et al. Person re-identification by multi-channel parts-based CNN with improved triplet loss function . [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE. 2016, 1335-1344.

【7】Chen B, Zha Y F, Li Y Q, et al. Person re-identification based on convolutional neural network discriminative feature learning [J]. Acta Optica Sinica. 2018, 38(7): 0720001.
陈兵, 查宇飞, 李运强, 等. 基于卷积神经网络判别特征学习的行人重识别 [J]. 光学学报. 2018, 38(7): 0720001.

【8】Wu L, Shen C. -06-20)[2019-06-25] . https:∥arxiv.xilesou. 2016, top/abs/1601: 07255.

【9】Ahmed E, Jones M, Marks T K. An improved deep learning architecture for person re-identification . [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE. 2015, 3908-3916.

【10】Varior R R, Shuai B, Lu J W, et al. A Siamese long short-term memory architecture for human re-identification [M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer. 2016, 9911: 135-153.

【11】Zhao H Y, Tian M Q, Sun S Y, et al. Spindle Net: person re-identification with human body region guided feature decomposition and fusion . [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE. 2017, 907-915.

【12】Wei L, Zhang S, Yao H, et al. Glad: global-local-alignment descriptor for pedestrian retrieval . [C]∥Proceedings of the 25th ACM International Conference on Multimedia, October 23-27, 2017, Mountain View, California, USA. New York: ACM. 2017, 420-428.

【13】Sun Y F, Zheng L, Yang Y, et al. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline) [M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer. 2018, 112008: 501-518.

【14】Zeiler M D, Fergus R. Visualizing and understanding convolutional networks [J]. Computer. 2013, 8689: 818-833.

【15】Zheng L, Shen L Y, Tian L, et al. Scalable person re-identification: a benchmark . [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE. 2015, 1116-1124.

【16】Ristani E, Solera F, Zou R, et al. Performance measures and a data set for multi-target, multi-camera tracking [M]. ∥Hua G, Jégou H. Computer vision-ECCV 2016 Workshops. Lecture notes in computer science. Cham: Springer. 2016, 9914: 17-35.

【17】He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition . [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE. 2016, 770-778.

【18】Yu W, Yang K Y, Yao H X, et al. Exploiting the complementary strengths of multi-layer CNN features for image retrieval [J]. Neurocomputing. 2017, 237: 235-241.

【19】Hermans A, Beyer L. -11-21)[2019-06-25] . https:∥arxiv.xilesou. 2017, top/abs/1703: 07737.

【20】Felzenszwalb F, Girshick B. McAllester D, et al. Object detection with discriminatively trained part-based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010, 32(9): 1627-1645.

【21】Wang Y, Wang L Q, You Y R, et al. Resource aware person re-identification across multiple resolutions . [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE. 2018, 8042-8051.

【22】Sun Y F, Zheng L, Deng W J, et al. SVDNet for pedestrian retrieval . [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE. 2017, 3820-3828.

【23】Chen Y B, Zhu X T, Gong S G. Person re-identification by deep learning multi-scale representations . [C]∥2017 IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017, Venice, Italy. New York: IEEE. 2017, 2590-2600.

引用该论文

Liu Kewen,Fang Panpan,Xiong Hongxia,Liu Chaoyang,Ma Yuan,Li Xiaojun,Chen Yalei. Person Re-Identification Based on Multi-Layer Feature[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081503

刘可文,房攀攀,熊红霞,刘朝阳,马圆,李小军,陈亚雷. 基于多层级特征的行人重识别[J]. 激光与光电子学进展, 2020, 57(8): 081503

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