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Person re-identification in the real scene based on the deep learning

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Abstract

Person re-identification aims at automatically retrieving a person of interest across multiple non-overlapping camera views. Because of increasing demand for real-world applications in intelligent video surveillance, person re-identification has become an important computer vision task and achieved high performance in recent years. However, the traditional person re-identification research mainly focus on matching cropped pedestrian images between queries and candidates on commonly used datasets and divided into two steps: pedestrian detection and person re-identification, there is still a big gap with practical applications. Under the premise of model optimization, based on the existing object detection and person re-identification, this paper achieves a one-step search of the specific pedestrians in the whole images or video sequences in the real scene. The experimental results show that our method is effective in commonly used datasets and has achieved good results in real-world applications, such as finding criminals, cross-camera person tracking, and activity analysis.

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References

  1. He L, Liao X, Liu W et al. (2020) FastReID: a pytorch toolbox for real-world person re-identification. arXiv: 2006.02631v4

  2. Zhou K, Yang Y, Cavallaro A, et al. (2019) Omni-scale feature learning for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3702–3712

  3. Felzenszwalb PF, Girshick RB, McAllester D et al. (2010) Object detection with discriminatively trained part based models. In: IEEE transactions on pattern analysis and machine intelligence, pp. 1627–1645

  4. Ren S, He K, Girshick R, et al. (2017) Faster R-CNN: towards real-time object detection with region proposal networks. In: IEEE transactions on pattern analysis and machine intelligence, pp. 1137–1149

  5. Girshick R, Donahue J, Darrell T (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 580–587

  6. Girshick R (2015) Fast R-CNN. In: The IEEE international conference on computer vision (ICCV), pp. 1440–1448

  7. Redmon J, Divvala S, Girshick R (2016) You only look once: unified, realTime object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 779–788

  8. Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement. arXiv: 1804.02767

  9. Li W, Zhao R, Xiao T, et al. (2014) DeepReID: deep filter pairing neural network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 152–159

  10. Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE international workshop on performance evaluation for tracking and surveillance (PETS)

  11. 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 (ICCV), pp. 1116–1124

  12. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp. 3754–3762

  13. Wei L, Zhang S, Gao W, et al. (2018) Person transfer GAN to bridge domain Gap for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 79–88

  14. Xu Y, Ma B, Huang R, et al. (2014) Person search in a scene by jointly modeling people commonness and person uniqueness. In: Proceedings of the 22nd ACM international conference on multimedia, pp. 937–940

  15. Xiao T, Li S, Wang B, et al. (2016) End-to-end deep learning for person search. arXiv: 1604.01850v1

  16. Xiao T, Li S, Wang B, et al. (2017) Joint detection and identification feature learning for person search. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 3415–3424

  17. Zheng L, Zhang Z, Sun M, et al. (2017) Person re-identification in the wild, In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 3346–3355

  18. Zhu M, Gong S, Qian Z, et al. (2020) Person re-identification on mobile devices based on deep learning. In: The 8th IIAE international conference on industrial application engineering 2020 (ICIAE), pp. 253–260

  19. Sun Y, Zheng L, Yang Y, et al. (2018) Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European conference on computer vision (ECCV), pp. 480–496

  20. Luo H, Gu Y, Liao X, et al. (2019) Bag of tricks and a strong baseline for deep person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 4321–4329

  21. 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 (CVPR), pp. 770–778

  22. Zhu M, Gong S, Qian Z, et al. (2019) A brief review on cycle generative adversarial networks. In: The 7th IIAE international conference on intelligent systems and image processing (ICISIP), pp. 235–242

  23. Wang G, Yang S, Liu H, et al. (2020) High-order information matters: learning relation and topology for occluded person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 6449–6458

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61972059, Grant 61702055, and Grant 61773272, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191474 and Grant BK20191475, in part by the Natural Science Foundation of Jiangsu Province in China under grant No. BK20191475, in part by the fifth phase of “333 Project" scientific research funding project of Jiangsu Province in China under grant No. BRA2020306, and in part by the Qing Lan Project of Jiangsu Province in China under Grant No. 2019.

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Authors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, 804-8550, Japan

    Miaomiao Zhu, Seiichi Serikawa & Lifeng Zhang

  2. Changshu Institute of Technology, Changshu, Jiangsu, 215500, China

    Shengrong Gong & Zhenjiang Qian

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  1. Miaomiao Zhu
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  2. Shengrong Gong
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Correspondence to Miaomiao Zhu.

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This work was presented in part at the 26th International Symposium on Artificial Life and Robotics (Online, January 21–23, 2021).

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Zhu, M., Gong, S., Qian, Z. et al. Person re-identification in the real scene based on the deep learning. Artif Life Robotics 26, 396–403 (2021). https://doi.org/10.1007/s10015-021-00689-9

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