Abstract
Partial person ReID tasks have become a research focus recently for it is challenging but significant in practical applications. The major difficulty within partial person ReID is that only incomplete and even noisy person features are available for extraction and matching, which puts forward higher requirement to model robustness. To settle down this problem, our paper proposes a novel IRRFE-ReID model, which includes two major innovations, the interesting receptive region selection module and the feature excitation module. The former module can adaptively select the region of interest from original image while the latter one is applied to distinguish representative person features and weight them during matching. Proven by ablation analysis, these two modules are embeddable and considerably conducive for partial person ReID tasks. Additionally, our IRRFE-ReID model achieves the state-of-the-art performance in two mainstream partial person datasets, PartialReID and PartialiLids, with its Rank1 reaching 85.7% and 74.8% respectively.
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References
Gong, S., et al.: Person re-identification. Adv. Comput. Vis. Pattern Recognit. 42(7), 301–313 (2014)
Leng, Q., Ye, M., Tian, Q.: A survey of open-world person re-identification. IEEE Trans. Circuits Syst. Video Technol. 30(4), 1092–1108 (2019)
Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future (2016)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP) (2017)
Cai, H., Wang, Z., Cheng, J.: Multi-scale body-part mask guided attention for person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
Zhang, Z., et al.: Densely semantically aligned person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Jin, X., et al.: Semantics-aligned representation learning for person re-identification (2020)
He, L., et al.: Fastreid: a pytorch toolbox for general instance re-identification. arXiv preprint arXiv:2006.02631 6(7), 8 (2020)
Wang, G.A., et al.: High-order information matters: learning relation and topology for occluded person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Zhu, S., et al.: Partial person re-identification with two-stream network and reconstruction. Neurocomputing 398, 453–459 (2020)
Gao, L., et al.: Texture semantically aligned with visibility-aware for partial person re-identification. In: Proceedings of the 28th ACM International Conference on Multimedia (2020)
Gao, Z., et al.: DCR: a unified framework for holistic/partial person ReID. IEEE Trans. Multimedia (2020)
Luo, H., et al.: Stnreid: deep convolutional networks with pairwise spatial transformer networks for partial person re-identification. IEEE Trans. Multimedia 22(11), 2905–2913 (2020)
Kortylewski, A., et al.: Compositional convolutional neural networks: a deep architecture with innate robustness to partial occlusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Kortylewski, A., et al.: Compositional convolutional neural networks: a robust and interpretable model for object recognition under occlusion. Int. J. Comput. Vis. 1–25 (2020)
Jia, W., et al.: Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot. Comput. Electron. Agri. 172, 105380 (2020)
Yang, Q., et al.: Focus on the visible regions: semantic-guided alignment model for occluded person re-identification. Sensors 20(16), 4431 (2020)
Han, C., Gao, C., Sang, N.: Keypoint-based feature matching for partial person re-identification. In: 2020 IEEE International Conference on Image Processing (ICIP). IEEE (2020)
Ye, M., et al.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. (2021)
Lawen, H., et al.: Compact network training for person ReID. In: Proceedings of the 2020 International Conference on Multimedia Retrieval (2020)
Pathak, P.: Fine-grained re-identification. arXiv preprint arXiv:2011.13475 (2020)
Chen, Q., Zhang, W., Fan, J.: Cluster-level feature alignment for person re-identification. arXiv preprint arXiv:2008.06810 (2020)
Zhuo, J., et al.: Occluded person re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2018)
Zhuo, J., Lai, J., Chen, P.: A novel teacher-student learning framework for occluded person re-identification. arXiv preprint arXiv:1907.03253 (2019)
Zheng, W.-S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: CVPR 2011. IEEE (2011)
Saghafi, M.A., et al.: Review of person re-identification techniques. IET Comput. Vis. 8(6), 455–474 (2014)
Liao, S., et al.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32(6–7), 379–390 (2014)
Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., Li, S.Z.: Salient color names for person re-identification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 536–551. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_35
Sun, K., et al.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Cao, Z., et al.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172–186 (2019)
Wang, R., Yan, J., Yang, X.: Learning combinatorial embedding networks for deep graph matching. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Zanfir, A., Sminchisescu, C.: Deep learning of graph matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Caetano, T.S., et al.: Learning graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1048–1058 (2009)
Gao, Z., et al.: Deep spatial pyramid features collaborative reconstruction for partial person ReID. In: Proceedings of the 27th ACM International Conference on Multimedia (2019)
Miao, J., et al.: Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Sun, Y., et al.: Perceive where to focus: Learning visibility-aware part-level features for partial person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_30
Li, J., et al.: Crowdpose: efficient crowded scenes pose estimation and a new benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019.)
Zhong, Y., Wang, X., Zhang, S.: Robust partial matching for person search in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Fu, Y., et al.: Horizontal pyramid matching for person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
Zheng, W.-S., et al.: Partial person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
He, L., et al.: Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Tan, H., et al.: Incomplete descriptor mining with elastic loss for person re-identification. IEEE Trans. Circuits Syst. Video Technol. (2021)
Gao, S., et al.: Pose-guided visible part matching for occluded person ReID. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Tan, H., et al.: MHSA-Net: multi-head self-attention network for occluded person re-identification. arXiv preprint arXiv:2008.04015 (2020)
He, L., et al.: Recognizing partial biometric patterns. arXiv preprint arXiv:1810.07399 (2018)
He, L., et al.: Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Miao, J., Wu, Y., Yang, Y.: Identifying visible parts via pose estimation for occluded person re-identification. IEEE Trans. Neural Netw. Learn. Syst. (2021)
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Meng, Q., Li, T., Ji, S., Zhu, S., Gu, J. (2021). Interesting Receptive Region and Feature Excitation for Partial Person Re-identification. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_24
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