Abstract
Person re-identification (re-ID) has attracted tremendous attention in the field of computer vision, especially in intelligent visual surveillance (IVS). The propose of re-ID is to retrieval the interest person across different cameras. There are still lots of challenges and difficulties that are the same appearance such as clothes, the lens distance, various poses and different shooting angles, all of which influence the performance of re-ID. In this paper, we propose a novel architecture, called global deep convolutional network (GDCN), which applies classical convolutional network as the backbone network and calculates the similarity between query and gallery. We evaluate the proposed GDCN on three large-scale public datasets: Market-1501 by 92.72% in Rank-1 and 88.86% in mAP, CHUK03 by 60.78% in Rank-1 and 62.47% in mAP, DukeMTMC-re-ID by 82.22% in Rank-1 and 77.99% in mAP, respectively. Besides, we compare the experimental results with previous work to verify the state-of-art performance of the proposed method that is implemented by NVIDIA Ge-Force GTX 1080Ti.
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
Bedagkar-Gala A, Shah SK (2014) A survey of approaches and trends in person re-identification. Image Vis Comput 32(4):270–286
Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. arXiv preprint arXiv:1610.02984
Barbosa IB, Cristani M, Caputo B et al (2018) Looking beyond appearances: synthetic training data for deep CNNS in re-identification. Comput Vis Image Underst 167:50–62
Zhao H, Tian M, Sun S et al (2017) Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1077–1085
Su C, Li J, Zhang S et al (2017) Pose-driven deep convolutional model for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3960–3969
Zheng Z, Zheng L, Yang Y (2018) Pedestrian alignment network for large-scale person re-identification. IEEE Trans Circ Syst Video Technol
Wei L, Zhang S, Yao H et al (2017) Glad: global-local-alignment descriptor for pedestrian retrieval. In: Proceedings of the 2017 ACM on multimedia conference. ACM, pp 420–428
Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826
He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Ye J (2011) Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math Comput Model 53(1–2):91–97
Zheng L, Shen L, Tian L et al (2015) Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124
Zhong Z, Zheng L, Cao D et al (2017) Re-ranking person re-identification with k-reciprocal encoding. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3652–3661
Li W, Zhao R, Xiao T et al (2014) Deepre-ID: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 152–159
Ristani E, Solera F, Zou R et al (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision. Springer, Cham, pp 17–35
Ristani E, Tomasi C (2018) Features for multi-target multi-camera tracking and re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6036–6046
Sun Y, Zheng L, Deng W et al (2017) SVDNet for pedestrian retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 3800–3808
Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294
Acknowledgements
This work is supported by National Natural Science Foundation of China (Project 61471066) and the open project fund (No. 201600017) of the National Key Laboratory of Electromagnetic Environment, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fu, M. et al. (2020). Global Deep Feature Representation for Person Re-Identification. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-9409-6_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9408-9
Online ISBN: 978-981-13-9409-6
eBook Packages: EngineeringEngineering (R0)