Abstract
The field of person re-identification remains a challenging topic in video surveillance and public security because it is facing many problems related to the variations of the position, background and brightness scenes. In order to minimize the impact of those variations, we introduce in this work a multi-stream re-identification system based on the fusion of local and global features. The proposed system uses first a body partition segmentation network (SEG-CNN) to segment three different body regions (the whole body part, the middle and the down body parts) that will represent local features. While the original image will be used to extract global features. Second, a multi-stream fusion framework is performed to fuse the outputs of the individual streams and generate the final predictions. We experimentally prove that the multi-stream combination method improves the recognition rates and provides better results than classic fusion methods. In the rank-1/mAP, the improvement is of \(7,24 \%\)/9, 5 for the Market-1501 benchmark dataset.
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References
Atrey, P.K., Hossain, M.A., El Saddik, A., Kankanhalli, M.S.: Multimodal fusion for multimedia analysis: a survey. Multimedia Syst. 16(6), 345–379 (2010). https://doi.org/10.1007/s00530-010-0182-0
Bialkowski, A., Denman, S., Sridharan, S., Fookes, C., Lucey, P.: A database for person re-identification in multi-camera surveillance networks. In: International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 1–8. IEEE (2012)
Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: Computer Vision and Pattern Recognition (CVPR). pp. 1335–1344 (2016)
Cho, Y.J., Yoon, K.J.: Improving person re-identification via pose-aware multi-shot matching. In: Computer Vision and Pattern Recognition (CVPR), pp. 1354–1362 (2016)
Gheissari, N., Sebastian, T.B., Hartley, R.: Person reidentification using spatiotemporal appearance. In: Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1528–1535. IEEE (2006)
Ghorbel, M., Ammar, S., Kessentini, Y., Jmaiel, M.: Improving person re-identification by background subtraction using two-stream convolutional networks. In: Karray, F., Campilho, A., Yu, A. (eds.) ICIAR 2019. LNCS, vol. 11662, pp. 345–356. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27202-9_31
Gong, S., Cristani, M., Yan, S., Loy, C.C.: Person Re-Identification, 1st edn., p. 445. springer, London (2014). https://doi.org/10.1007/978-1-4471-6296-4
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Hu, X., Jiang, Z., Guo, X., Zhou, Y.: Person re-identification by deep learning muti-part information complementary. In: IEEE International Conference on Image Processing (ICIP), pp. 848–852 (2018)
Huang, H., Li, D., Zhang, Z., Chen, X., Huang, K.: Adversarially occluded samples for person re-identification. In: Computer Vision and Pattern Recognition (CVPR), pp. 5098–5107 (2018)
Huang, Y., Zha, Z.J., Fu, X., Zhang, W.: Illumination-invariant person re-identification. In: ACM International Conference on Multimedia, pp. 365–373 (2019)
Huang, Z., et al.: Contribution-based multi-stream feature distance fusion method with k-distribution re-ranking for person re-identification. IEEE Access 7, 35631–35644 (2019)
Karanam, S., Li, Y., Radke, R.J.: Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: IEEE International Conference on Computer Vision, pp. 4516–4524 (2015)
Kittler, J.: Combining classifiers: a theoretical framework. Pattern Anal. Appl. 1(1), 18–27 (1998). https://doi.org/10.1007/BF01238023
Li, D., Chen, X., Zhang, Z., Huang, K.: Learning deep context-aware features over body and latent parts for person re-identification. In: Computer Vision and Pattern Recognition (CVPR), pp. 384–393 (2017)
Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: Joint body parsing & pose estimation network and a new benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 41, 871–885 (2018)
Mansouri, N., Ammar, S., Kessentini, Y.: Improving person re-identification by combining Siamese convolutional neural network and re-ranking process. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2019)
Quan, R., Dong, X., Wu, Y., Zhu, L., Yang, Y.: Auto-ReID: searching for a part-aware convnet for person re-identification. arXiv preprint arXiv:1903.09776 (2019)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Tian, M., et al.: Eliminating background-bias for robust person re-identification. In: Computer Vision and Pattern Recognition (CVPR), pp. 5794–5803 (2018)
Varior, R.R., Haloi, M., Wang, G.: Gated Siamese convolutional neural network architecture for human re-identification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 791–808. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_48
Wei, L., Zhang, S., Yao, H., Gao, W., Tian, Q.: GLAD: global-local-alignment descriptor for scalable person re-identification. IEEE Trans. Multimedia 21(4), 986–999 (2018)
Weinrich, C., Volkhardt, M., Gross, H.M.: Appearance-based 3D upper-body pose estimation and person re-identification on mobile robots. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 4384–4390. IEEE (2013)
Yao, H., Zhang, S., Hong, R., Zhang, Y., Xu, C., Tian, Q.: Deep representation learning with part loss for person re-identification. IEEE Trans. Image Process. 28(6), 2860–2871 (2019)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision (ICCV), pp. 1116–1124 (2015)
Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person reidentification. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 14(1), 13 (2018)
Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: Computer Vision and Pattern Recognition (CVPR), pp. 1318–1327. IEEE (2017)
Acknowledgement
This project is carried out under the MOBIDOC scheme, funded by the EU through the EMORI program and managed by the ANPR. We thank Anavid for assistance. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
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Ghorbel, M., Ammar, S., Kessentini, Y., Jmaiel, M., Chaari, A. (2021). Fusing Local and Global Features for Person Re-identification Using Multi-stream Deep Neural Networks. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_6
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