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http://dx.doi.org/10.7471/ikeee.2019.23.4.1166

Advanced PersonNet for Person Re-Identification  

Park, Seong-Hyeon (Dept. of Embedded Systems Engineering, Incheon National University)
Kang, Seok-Hoon (Dept. of Embedded Systems Engineering, Incheon National University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1166-1174 More about this Journal
Abstract
This paper propose and experiment advanced PersonNet, a human identification model, with advanced performance. We apply the inception layer to extract feature points, and increase the existing 32 feature points to 154. Also, we modify the CND method used by PersonNet to mitigate asymmetry, and apply weights to the feature map of pedestrian images in three parts, thereby making the features more distinct. Three databases were used for performance evaluation : CUHK01, CUHK03 and Market-1501. The experiment results showed 27-31% improvement in performance.
Keywords
PersonNet; CNN; Inception Layer; Cross neighborhood difference; Person re-Identification;
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1 L. Zheng, Y. Huang, H. Lu and Y. Yang, "Pose Invariant Embedding for Deep Person Re-Identification," IEEE Transactions on Image Processing, pp.4500-4509, 2019. DOI: 10.1109/TIP.2019.2910414   DOI
2 W. Li, R. Zhao, and X. Wang, "Human reidentification with transferred metric learning," Asian conference on computer vision. Springer, Berlin, Heidelberg, pp.31-44, 2012. DOI: 10.1007/978-3-642-37331-2_3
3 W. Li, R. Zaho, T. Xiao, and X. Wang, "Deepreid: Deep filter pairing neural network for person re-identification," Proceedings of the IEEE conference on computer vision and pattern recognition. pp.152-159, 2014. DOI: 10.1109/CVPR.2014.27
4 L. Zheng, L.Shen, L. Tian, S.wang, J. Wang, and Q. Tian, "Scalable person re-identification: A benchmark," Proceedings of the IEEE international conference on computer vision. pp.1116-1124, 2015. DOI: 10.1109/ICCV.2015.133
5 G. Koch, R. Zemel, and R. Salakhutdinov, "Siamese neural networks for one-shot image recognition," A thesis for the degree of Master of Science, University of Toronto, 2015.
6 S. Wu, Y. C. Chen, X. Li, A. C. Wu, J. J. You and W. S. Zheng, "An Enhanced Deep Feature Representation for Person Re-identification," 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp.1-8, 2016.
7 L. Ma, H. Liu, L. Hu, C. Wang, Q. Sun, "Orientation driven bag of appearances for person re-identification," arXiv preprint arXiv:1605.02464, [Online]. Available: https://arxiv.org/abs/1605.02464, 2016.
8 D. Baltieri, R. Vezzani, R. Cucchiara, "3DPes: 3D People Dataset for Surveillance and Forensics," Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding. ACM, pp.59-64, 2011. DOI: 10.1145/2072572.2072590
9 S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," arXiv preprint arXiv: 1502.03167, [Online]. Available: https://arxiv.org/abs/1502.03167, 2015.
10 L. Wu, C. Shen, and A. v. d. Hengel, "Personnet: Person reidentification with deep convolutional neural networks.," arXiv preprint arXiv:1601.07255, [Online]. Available: https://arxiv.org/abs/1601.07255, 2016
11 K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition", arXiv preprint arXiv:1409.1556, [Online]. Available: https://arxiv.org/abs/1409.1556, 2014.
12 C. Szegedy, W. Liu, Y. Jia, P. sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going deeper with convolutions," Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1-9, 2015.
13 E. Ahmed, M. Jones, and T. K. Marks, "An Improved Deep Learning Architecture for Person Re-Identification," Proceedings of the IEEE conference on computer vision and pattern recognition. pp.3908-3916, 2015. DOI: 10.1109/CVPR.2015.7299016