ABSTRACT
Person re-identification(Re-ID) using deep learning has made great progress in the past few years, but there is one problem that many state-of-the-art Re-ID methods all use a complex network most of which use the structure of multi-branch and multi-loss function. At present, the database used for Person re-identification is relatively small. This complex network structure may bring a problem that although current methods may perform well in the small databases, but there may be some problems of overfitting problem, once applied in the bigger dataset or real scene these complex methods may perform not well. So this paper mainly proposes a new powerful baseline network. This end-to-end network only uses a global feature and does not use multi-branch structure, but achieves state-of-the-art level. The key point is that this network has good improvement potential to adapt to larger datasets and even practical application scenarios.
- Zheng, L., Yang, Y., & Hauptmann, A. G. (2016). Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984.Google Scholar
- Zheng, Z., Zheng, L., & Yang, Y. (2018). A discriminatively learned cnn embedding for person reidentification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 14(1), 13.Google ScholarDigital Library
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770--778).Google ScholarCross Ref
- Hermans, A., Beyer, L., & Leibe, B. (2017). In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737.Google Scholar
- 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).Google ScholarCross Ref
- Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818--2826).Google ScholarCross Ref
- Kalayeh, M. M., Basaran, E., Gökmen, M., Kamasak, M. E., & Shah, M. (2018). Human semantic parsing for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1062--1071).Google ScholarCross Ref
- Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Google Scholar
- Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable person re-identification: A benchmark. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1116--1124).Google ScholarCross Ref
- 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 (pp. 3754--3762).Google ScholarCross Ref
- Sun, Y., Zheng, L., Deng, W., & Wang, S. (2017). Svdnet for pedestrian retrieval. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3800--3808).Google ScholarCross Ref
- Xu, J., Zhao, R., Zhu, F., Wang, H., & Ouyang, W. (2018). Attention-aware compositional network for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2119--2128).Google ScholarCross Ref
- Si, J., Zhang, H., Li, C. G., Kuen, J., Kong, X., Kot, A. C., & Wang, G. (2018). Dual attention matching network for context-aware feature sequence based person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5363--5372).Google ScholarCross Ref
- Sun, Y., Zheng, L., Yang, Y., Tian, Q., & Wang, S. (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).Google ScholarCross Ref
- Saquib Sarfraz, M., Schumann, A., Eberle, A., & Stiefelhagen, R. (2018). A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 420--429).Google ScholarCross Ref
- Wang, G., Yuan, Y., Chen, X., Li, J., & Zhou, X. (2018, October). Learning discriminative features with multiple granularities for person re-identification. In 2018 ACM Multimedia Conference on Multimedia Conference (pp. 274--282). ACM.Google ScholarDigital Library
- Zheng, F., Sun, X., Jiang, X., Guo, X., Yu, Z., & Huang, F. (2018). A Coarse-to-fine Pyramidal Model for Person Re-identification via Multi-Loss Dynamic Training. arXiv preprint arXiv:1810.12193.Google Scholar
Index Terms
- An improved baseline for person re-identification
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