Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지
DOI QR코드

DOI QR Code

Advanced PersonNet for Person Re-Identification

사람 재인식을 위한 개선된 PersonNet

  • Park, Seong-Hyeon (Dept. of Embedded Systems Engineering, Incheon National University) ;
  • Kang, Seok-Hoon (Dept. of Embedded Systems Engineering, Incheon National University)
  • Received : 2019.11.08
  • Accepted : 2019.12.16
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

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.

이 논문에서는 사람 재식별 모델인 PersonNet의 성능을 개선하는 방법을 제안하고 실험한다. 특징점 추출을 위해 인셉션 레이어를 접목하여, 기존 32개의 특징점을 154개로 증가시켜 강화하였다. 또한, PersonNet에서 사용하는 CND 방식을 수정하여 비대칭성을 완화하였고, 보행자 이미지의 특징점을 3부분으로 나누어 가중치를 적용한 방법을 적용하여 특징을 더 뚜렷하게 파악하도록 하였다. 성능 평가를 위해 CUHK01, CUHK03 그리고 Market-1501 3가지의 데이터베이스를 사용하였고 실험 결과 27~31% 성능이 개선되었다.

Keywords

References

  1. 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.
  2. 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
  3. 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.
  4. 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.
  5. 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
  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. 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
  8. 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
  9. 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
  10. 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
  11. 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.
  12. 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
  13. 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.