Abstract
In recent years, artificial intelligence technology has made breakthrough progress. It is widely used in person re-identification(Re-ID), but its accuracy still needs to be improved. This paper proposes a person re-identification(Re-ID) model based on 3D data augmentation and designs and implements edge computing deployment. The 3D human body model is put into the 3D scene and take automatic virtual photography to obtain the simulation image data. The neural network model is trained through the expanded data set of 3D simulation, and its improvement is significant. Based on this, an onboard scheme of artificial intelligence edge computing based on the Rock chips 3588 was designed to meet the practical needs of person re-identification(Re-ID) applications. This scheme is widely compatible with standard IP cameras based on RTSP/RTMP streams, supports person re-identification of visible and infrared video streams, can control the PTZ through the network interface, and can be updated and iterated remotely through the network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future, arXiv preprint arXiv:1610.02984 (2016)
Karanam, S., Gou, M., Wu, Z., Rates-Borras, A., Camps, O., Radke, R.J.: A systematic evaluation and benchmark for person reidentification: Features, metrics, and datasets. In: IEEE TPAMI (2018)
Chen, Y.-C., Zhu, X., Zheng, W.-S., Lai, J.-H.: Person re-identification by camera correlation aware feature augmentation. IEEE TPAMI 40(2), 392–408 (2018)
Ni, H., Song, J., Zhu, X., et al.: Camera-agnostic person re-identification via adversarial disentangling learning (2021)
Fu, X., Lai, X.: Unsupervised person re-identification via multi-order cross-view graph adversarial network. IEEE Access 9, 22264–22273 (2021)
Zhou, D.W., Gu, X.X., Wang, Y., et al.: Shader technology based on physical rendering (2022)
Wei, L., Rui, Z., Tong, X., et al.: DeepReID: deep filter pairing neural network for person re-identification. In: Computer Vision & Pattern Recognition, IEEE (2014)
Zhong, S., Bao, Z., Gong, S., et al.: person reidentification based on pose-invariant feature and B-KNN Reranking. IEEE Trans. Comput. Soc. Syst. 8(5), 1272–1281 (2021)
Ming, Z., Zhu, M., Wang, X., et al.: Deep learning-based person re-identification methods: a survey and outlook of recent works (2021)
Zhuang, W., Wen, Y., Zhang, S.: Joint optimization in edge-cloud continuum for federated unsupervised person re-identification (2021)
Integrating coarse granularity part-level features with supervised global-level features for person re-identification (2021)
Wang, X., Li, S., Liu, M., et al.: Multi-expert adversarial attack detection in person re-identification using context inconsistency. arXiv e-prints (2021)
Zhang, C., Wu, L., Wang, Y.: Crossing generative adversarial networks for cross-view person re-identification. Elsevier (2019)
Dai, C., Wang, H., Ni, T., et al.: Person re-identification based on deep convolutional generative adversarial network and expanded neighbor reranking. J. Comput. Res. Dev. 2019(8)
Ye, M., Shen, J., Shao, L.: Visible-infrared person re-identification via homogeneous augmented tri-modal learning. IEEE Trans. Inf. Forensics Secur. 16, 728–739 (2020)
Acknowledgments
This research was funded by the National Key Laboratory Foundation of China grant number TCGZ2020C004 and 202020429036.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bian, Z., Ma, L., Li, J., Xu, T. (2023). 3D Data Augmented Person Re-identification and Edge-based Implementation. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. AIC 2022. Lecture Notes in Electrical Engineering, vol 871. Springer, Singapore. https://doi.org/10.1007/978-981-99-1256-8_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-1256-8_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1255-1
Online ISBN: 978-981-99-1256-8
eBook Packages: Computer ScienceComputer Science (R0)