Person Re-identification on Mobile Devices Based on Deep Learning

  • Miaomiao Zhu Kyushu Institute of Technology, Kitakyushu
  • Shengrong Gong Changshu Institute of Technology
  • Zhenjiang Qian Changshu Institute of Technology
  • Seiichi Serikawa Kyushu Institute of Technology
  • Lifeng Zhang Kyushu Institute of Technology

Abstract

Person re-identification, as an important supplement to face recognition, it can solve the cross-camera and cross-scene pedestrian recognition and retrieval, determining whether there is a specific pedestrian to be detected in the image or video sequence. At present, many person re-identification researches are mainly carried out through experimental verification and evaluation on large person re-identification datasets such as Market-1501, DukeMTMC-reID, MSMT17, and CUHK03. In this paper, based on the existing deep learning person re-identification research and combining with the actual application scenarios on the premise of analyzing the technical feasibility, we propose a complete process for person re-identification with mobile devices, which aims to combine pedestrian detection with person re-identification to perform real-time pedestrian detection and query. In this process, not only the features extracted by pedestrians can be reused, but also the research on person re-identification can be applied effectively, such as tracking criminals and searching for missing children.

Author Biography

Miaomiao Zhu, Kyushu Institute of Technology, Kitakyushu

Department of Electrical and Electronic Engineering

References

G. E. Hinton, S. Osindero and Y. Teh, “A fast learning algorithm for deep belief nets”, Neural Computation, Vol.18, No.7, pp.1527-1554, 2006. DOI: 10.1162/neco.2006.18.7.1527

G.E.HintonandR.R.Salakhutdinov,“Reducingthedimen- sionality of data with neural networks”, Science, Vol.313, No.5786, pp.504-507, 2006. DOI: 10.1126/science.1127647

Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, “Gradient- based learning applied to document recognition”, Proceed- ings of the IEEE, Vol.86, No.11, pp.2278-2324, 1998. DOI: 10.1109/5.726791

A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Advances in Neural Information Processing Systems 25 (NIPS), vol.1, pp.1097-1105, 2012.

X. Wu, D. Sahoo and S. C. H. Hoi, “Recent Ad- vances in Deep Learning for Object Detection”, Neurocomputing, Vol.396, pp.39-64, 2020. DOI: 10.1016/j.neucom.2020.01.085

A. Bochkovskiy, C. Wang and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection”, arXiv:2004.10934, 2020.

X. Long, K. Deng, G. Wang, Y. Zhang, Q. Dang, Y. Gao, H. Shen, J. Ren, S. Han, E. Ding and S. Wen, “PP-YOLO: An Effective and Efficient Implementation of Object Detector”, arXiv:2007.12099, 2020.

W. Zajdel, Z. Zivkovic and B. J. A. Krose, “Keeping Track of Humans: Have I Seen This Person Before?”, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp.2081-2086, 2005, DOI: 10.1109/ROBOT.2005.1570420

D. Gray, S. Brennan and H. Tao: “Evaluating appearance models for recognition, reacquisition, and tracking”, In Proc. IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol.3, No.5, 2007.

W. Li, R. Zhao, 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 (CVPR), pp.152-159, 2014. DOI: 10.1109/CVPR.2014.27

C.C. Loy, T. Xiang and S.Gong, “Multi-camera activity correlation analysis”, 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1988-1995, 2009. DOI: 10.1109/CVPR.2009.5206827

L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang and Q. Tian, “Scalable person re-identification: A bench- mark”, Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp.1116-1124, 2015. DOI: 10.1109/ICCV.2015.133

Z. Zheng, L. Zheng and Y. Yang, “Unlabeled Samples Generated by Gan Improve the Person Re-identification Baseline in vitro”, Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp.3774-3782, 2017. DOI: 10.1109/ICCV.2017.405

L. Wei, S. Zhang, W. Gao and Q. Tian, “Person Transfer GAN to Bridge Domain Gap for Person Re-Identification”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.79-88, 2018. DOI: 10.1109/CVPR.2018.00016

M. Zhu, S. Gong, Z. Qian and L. Zhang, “A Brief Review on Cycle Generative Adversarial Networks”, In The 7th IIAE International Conference on Intelligent Systems and Image Processings (ICISIP), pp.235-242, 2019. DOI: 10.12792/icisip2019.046

J. Redmon and A. Farhadi: “YOLOv3: An Incremental Im- provement”, arXiv:1804.02767, 2018.

H. Luo, Y. Gu, X. Liao, S. Lai and W. Jiang, “Bag of Tricks and A Strong Baseline for Deep Person Re-identification”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), pp.1487- 1495, 2019. DOI: 10.1109/CVPRW.2019.00190

K. Zhou, Y. Yang, A. Cavallaro and T. Xiang, “Omni-Scale Feature Learning for Person Re-Identification”, Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp.3701-3711, 2019. DOI: 10.1109/ICCV.2019.00380

M. Ye, J. Shen, G. lin, T. Xiang, L. Shao and S. C. H. Hoi, “Deep Learning for Person Re-identification: A Survey and Outlook”, arXiv:2001.04193v1, 2020.

Published
2021-01-24
How to Cite
Zhu, M., Gong, S., Qian, Z., Serikawa, S., & Zhang, L. (2021). Person Re-identification on Mobile Devices Based on Deep Learning. Journal of the Institute of Industrial Applications Engineers, 9(1), 26. https://doi.org/10.12792/jiiae.9.26
Section
Articles