区域块分割与融合的行人再识别

蒋建国; 杨宁; 齐美彬; 陈翠群

doi:10.11834/jig.180370

图像分析和识别 | 浏览量 : 0 下载量: 49 CSCD: 2

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区域块分割与融合的行人再识别
Person re-identification with region block segmentation and fusion
2019年24卷第4期页码：513-522
收稿：2018-06-07，

修回：2018-11-7，

纸质出版：2019-04-16
DOI： 10.11834/jig.180370
稿件说明：

移动端阅览

蒋建国, 杨宁, 齐美彬, 陈翠群. 区域块分割与融合的行人再识别[J]. 中国图象图形学报, 2019,24(4):513-522. DOI： 10.11834/jig.180370.

Jianguo Jiang, Ning Yang, Meibin Qi, Cuiqun Chen. Person re-identification with region block segmentation and fusion[J]. Journal of Image and Graphics, 2019, 24(4): 513-522. DOI： 10.11834/jig.180370.

摘要

目的

由于摄像机视角和成像质量的差异，造成行人姿态变化、图像分辨率变化和光照变化等问题的出现，从而导致同一行人在不同监控视频中的外观区别很大，给行人再识别带来很大挑战。为提高行人再识别的识别率，针对行人姿态变化问题，提出一种区域块分割和融合的行人再识别算法。

方法

首先根据人体结构分布，将行人图像划分为3个局部区域。然后根据各区域在识别过程中的作用不同，将GOG（Gaussian of Gaussian）特征、LOMO（local maximal occurrence）特征和KCCA（Kernel canonical correlation analysis）特征的不同组合作为各区域特征。接着通过距离测度算法学习对应区域之间的相似度，并通过干扰块剔除算法消除图像中出现的无效干扰块，融合有效区域块的相似度。最后将行人图像对的全局相似度和各局部区域相似度进行融合，实现行人再识别。

结果

在4个基准数据集VIPeR、GRID、PRID450S和CUHK01上进行了大量实验，其中Rank1（排名第1的搜索结果即为待查询人的比例）分别为62.85%、30.56%、71.82%和79.03%，Rank5分别为86.17%、51.20%、91.16%和93.60%，识别率均有显著提高，具有实际应用价值。

结论

提出的区域块分割和融合方法，能够去除图像中的无用信息和干扰信息，同时保留行人的有效信息并高效利用。该方法在一定程度上能够解决行人姿态变化带来的外观差异问题，大幅度地提升识别率。

Abstract

Objective

The person re-identification task is of great value in multi-target tracking and the target retrieval of multi-cameras. Thus

it has received increasing attention in the field of computer vision and widespread interest among researchers at home and abroad in recent years. The differences in camera viewing angles and imaging quality lead to variations in pedestrian posture

image resolution

and illumination. These variations make the appearance of the same pedestrian in various surveillance videos considerably different. This difference

in turn

causes severe interference in person re-identification. To improve the recognition rate of person re-identification and solve the posture changing problem

this study proposes a person re-identification algorithm with region block segmentation and fusion on the basis of human body structure information.

Method

First

according to the distribution of the human body structure

a pedestrian image is divided into three local regions:the head part (the H region)

the shoulder-knee part (the SK region)

and the leg part (the L region). These local regions are enlarged to the original image size using a bilinear interpolation method

which can enhance the expression of the regions and fully use the region information. Second

according to the different roles of each local region in the recognition process

the Gaussian of Gaussian (GOG) feature is extracted from the H and the L regions. The GOG feature

the local maximal occurrence (LOMO) feature

and the kernel canonical correlation analysis (KCCA) feature are extracted from the SK region because the SK region contains the most abundant information of pedestrian images. Extracting numerous features in the SK region can increase the diversity of the region information and strengthen the role of the region in the re-identification process. Third

the interference block removal (IBR) algorithm is used to eliminate the invalid blocks in the image and fuse the similarities of the effective blocks. Given the differences in posture and viewpoint

some objects might appear in one image and be absent in another image of the same person captured by another camera. Such objects may cause large changes in the color and texture information of the pedestrian's corresponding body regions. These changes result in disturbances to the recognition process. The regions in which such objects are located are called interference blocks in this study. By observing the location of the interference blocks

we find that the interference blocks are distributed from the shoulder to the knee of pedestrians. Therefore

the IBR algorithm uses the image of the SK region. According to the human body structure distribution

the IBR algorithm horizontally divides the SK region into the chest part (h1 block)

the lumbar part (h2 block)

and the leg part (h3 block); and vertically divides the region into the left-arm part (v1 block)

the torso part (v2 block)

and the right-arm part (v3 block). Then

the GOG feature

LOMO feature

and KCCA feature are extracted from each block. The three features of each block are fed to the similarity measure function to obtain the three similarities between the corresponding blocks. The three similarities of the same block are merged to form the final similarity of the block. When the final similarities of the six block (h1

v3) pairs are calculated

the similarities of the three horizontal block (h1

h3) pairs are compared to find the block with the smallest similarity

which is the interference block in the horizontal direction. The interference block in the vertical direction is found in the same manner. When the two interference blocks are removed

the influence of the interference block on the overall pedestrian similarity can be eliminated. After the interference blocks are removed

the similarities of the remaining four blocks are fused as the similarity of the SK region. Finally

the global similarity of the pedestrian image pair and the similarities of the three local regions (H

and SK) are combined to realize person re-identification.

Result

Many experiments are conducted on four benchmark datasets

namely

VIPeR

GRID

PRID450S

and CUHK01. The results of rank 1 (represents the proportion of queried people) for the four datasets are 62.85%

30.56%

71.82%

and 79.03%. The results of rank 5 are 86.17%

51.20%

91.16%

and 93.60%. The experimental results show the considerable improvement of recognition rates for the small and large datasets. Thus

the proposed algorithm offers practical application value.

Conclusion

Experimental results show that the proposed method can effectively express the image information of pedestrians. Furthermore

the proposed region block segmentation and fusion algorithm can remove useless and interference information in images as much as possible under the guidance of human body structure information. It can also preserve the effective information of pedestrians and use it effectively. This method can solve the differences in pedestrian appearance caused by changes in pedestrian posture to a certain extent and greatly improve recognition rates.

关键词

Keywords

references

Chu H F, Qi M B, Liu H, et al. Local region partition for person re-identification[J]. Multimedia Tools and Applications, 2017.[DOI:10.1007/s11042-017-4817-4]

Qi M B, Hu L F, Jiang J G, et al. Person re-identification based on multi-features fusion and independent metric learning[J]. Journal of Image and Graphics, 2016, 21(11):1464-1472.

齐美彬, 胡龙飞, 蒋建国, 等.多特征融合与独立测度学习的行人再识别[J].中国图象图形学报, 2016, 21(11):1464-1472. [DOI:10.11834/jig.20161106]

Qi M B, Wang C C, Jiang J G, et al. Person re-identification based on multi-feature fusion and alternating direction method of multipliers[J]. Journal of Image and Graphics, 2018, 23(6):827-836.

齐美彬, 王慈淳, 蒋建国, 等.多特征融合与交替方向乘子法的行人再识别[J].中国图象图形学报, 2018, 23(6):827-836. [DOI:10.11834/jig.170507]

You J J, Wu A C, Li X, et al. Top-push video-based person re-identification[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1345-1353.[ DOI:10.1109/CVPR.2016.150 http://dx.doi.org/10.1109/CVPR.2016.150 ]

Zheng W S, Li X, Xiang T, et al. Partial person re-identification[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 4678-4686.[ DOI:10.1109/ICCV.2015.531 http://dx.doi.org/10.1109/ICCV.2015.531 ]

Wei L H, Zhang S L, Yao H T, et al. GLAD: global-local-alignment descriptor for pedestrian retrieval[C]//Proceedings of the 25th ACM International Conference on Multimedia. Mountain View, CA, USA: ACM, 2017: 420-428.[ DOI:10.1145/3123266.3123279 http://dx.doi.org/10.1145/3123266.3123279 ]

Liu H, Jie Z Q, Jayashree K, et al. Video-based person re-identification with accumulative motion context[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(10):2788-2802.[DOI:10.1109/TCSVT.2017.2715499]

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]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 907-915.[ DOI:10.1109/CVPR.2017.103 http://dx.doi.org/10.1109/CVPR.2017.103 ]

Li D W, Chen X T, Zhang Z, et al. Learning deep context-aware features over body and latent parts for person re-identification[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 7398-7404.[ DOI:10.1109/CVPR.2017.782 http://dx.doi.org/10.1109/CVPR.2017.782 ]

Liao S C, Zhao G Y, Kellokumpu V, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010: 1301-1306.[ DOI:10.1109/CVPR.2010.5539817 http://dx.doi.org/10.1109/CVPR.2010.5539817 ]

Liao S C, Hu Y, Zhu X Y, et al. Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015: 2197-2206.[ DOI:10.1109/CVPR.2015.7298832 http://dx.doi.org/10.1109/CVPR.2015.7298832 ]

Matsukawa T, Okabe T, Suzuki E, et al. Hierarchical Gaussian descriptor for person re-identification[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1363-1372.[ DOI:10.1109/CVPR.2016.152 http://dx.doi.org/10.1109/CVPR.2016.152 ]

Lisanti G, Masi I, del Bimbo A. Matching people across camera views using kernel canonical correlation analysis[C]//Proceedings of 2014 International Conference on Distributed Smart Cameras. Venezia Mestre, Italy: ACM, 2014: #10.[ DOI:10.1145/2659021.2659036 http://dx.doi.org/10.1145/2659021.2659036 ]

Farenzena M, Bazzani L, Perina A, et al. Person re-identification by symmetry-driven accumulation of local features[C]//Proc eedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010: 2360-2367.[ DOI:10.1109/CVPR.2010.5539926 http://dx.doi.org/10.1109/CVPR.2010.5539926 ]

Bąk S, Corvee E, Bremond F, et al. Person re-identification using spatial covariance regions of human body parts[C]//Proceedings of the 7th IEEE International Conference on Advanced Video and Signal Based Surveillance. Boston, MA, USA: IEEE, 2010: 435-440.[ DOI:10.1109/AVSS.2010.34 http://dx.doi.org/10.1109/AVSS.2010.34 ]

Chen D P, Yuan Z J, Chen B D, et al. Similarity learning with spatial constraints for person re-identification[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1268-1277.[ DOI:10.1109/CVPR.2016.142 http://dx.doi.org/10.1109/CVPR.2016.142 ]

Zhang L, Xiang T, Gong S G. Learning a discriminative null space for person re-identification[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1239-1248.[ DOI:10.1109/CVPR.2016.139 http://dx.doi.org/10.1109/CVPR.2016.139 ]

Gray D, Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]//Proceedings of the 10th European Conference on Computer Vision. Marseille, France: Springer, 2008: 262-275.[ DOI:10.1007/978-3-540-88682-2_21 http://dx.doi.org/10.1007/978-3-540-88682-2_21 ]

Loy C C, Xiang T, Gong S G. Time-delayed correlation analysis for multi-camera activity understanding[J]. International Journal of Computer Vision, 2010, 90(1):106-129.[DOI:10.1007/s11263-010-0347-5]

Roth P M, Hirzer M, Köstinger M, et al. Mahalanobis distance learning for person re-identification[M]//Gong S G, Cristani M, Yan S C, et al. Person Re-Identification. London: Springer, 2014: 247-267.[ DOI:10.1007/978-1-4471-6296-4_12 http://dx.doi.org/10.1007/978-1-4471-6296-4_12 ]

Li W, Zhao R, Wang X G. Human reidentification with transferred metric learning[C]//Proceedings of the 11th Asian Conference on Computer Vision. Daejeon, Korea: Springer, 2012: 31-44.[ DOI:10.1007/978-3-642-37331-2_3 http://dx.doi.org/10.1007/978-3-642-37331-2_3 ]

Zhang J, Zhao X. Global-local metric learning for person re-identification[J]. Journal of Image and Graphics, 2017, 22(4):472-481.

张晶, 赵旭.整合全局-局部度量学习的人体目标再识别[J].中国图象图形学报, 2017, 22(4):472-481. [DOI:10.11834/jig.20170407]

Zhang N, Zhang F X, Wang Q, et al. Learning bidirectional relationship similarity function for person re-identification[J]. Computer Systems&Applications, 2018, 27(5):33-40.

张娜, 张福星, 王强, 等.基于双向关系相似度函数学习的行人再识别[J].计算机系统应用, 2018, 27(5):33-40. [DOI:10.15888/j.cnki.csa.006354]

Liu Q, Hou L, Peng Z Y. Invariant feature and kernel distance metric learning based person re-identification[J]. Journal of Image and Signal Processing, 2018, 7(2):65-73.

刘琦, 侯丽, 彭章友.基于不变特征和核距离度量学习的行人再识别[J].图像与信号处理, 2018, 7(2):65-73. [DOI:10.12677/JISP.2018.72008]

Wu S X, Chen Y C, Li X, et al. An enhanced deep feature representation for person re-identification[C]//Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision. Lake Placid, NY, USA: IEEE, 2016.[ DOI:10.1109/WACV.2016.7477681 http://dx.doi.org/10.1109/WACV.2016.7477681 ]