Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 91-97.doi: 10.6040/j.issn.1672-3961.0.2018.347

• Machine Learning & Data Mining • Previous Articles     Next Articles

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)

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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

CLC Number: 

  • TP391

Fig.1

Singular value decomposition pedestrian alignment network framework"

Table 1

Experimental results of rank-1 and mAP in different datasets"

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

Table 2

Experimental results comparison of rank-1 in SVDNet and singular value decomposition pedestrian alignment network"

%
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

Table 3

Experiment results comparison of rank-1(%) in PAN and SVDNet"

%
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

Table 4

Experimental results comparison of rank-1 and mAP in different methods on Market1501 and CUHK03 datasets"

%
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

Table 5

Experimental results comparison of rank-1 and mAP in different methods on DukeMTMC-reID dataset"

%
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.
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