[1] T. Van Phat, S. Alam, N. Lilith, P.N. Tran, and N.T. Binh, “Deep4air: A novel deep learning framework for airport airside surveillance”, In 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6). 2021.
[2] O. Elharrouss, N. Almaadeed, and S. Al-Maadeed, “A review of video surveillance systems”, Journal of Visual Communication and Image Representation, Vol. 77, p.103116, 2021.
[3] A. Alshammari, and D.B. Rawat, “Intelligent multi-camera video surveillance system for smart city applications”, In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0317-0323), 2019, IEEE.
[4] J. Vanus, J. Machac, R. Martinek, P. Bilik, J. Zidek, J. Nedoma, and M. Fajkus, “The design of an indirect method for the human presence monitoring in the intelligent building”, Human-centric Computing and Information Sciences, Vol. 8, No. 1, pp.1-44, 2018.
[5] G.K. Nayak, U. Shreemali, R.V. Babu, and A. Chakraborty, “Efficient person re-identification in videos using sequence lazy greedy determinantal point process (slgdpp)”, In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 4569-4573), 2019, IEEE.
[6] Q. Leng, M. Ye, and Q. Tian, “A survey of open-world person re-identification”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 30, No. 4, pp.1092-1108, 2019
[7] Gowsikhaa, D., Abirami, S. and Baskaran, R., (2014) ‘Automated human behavior analysis from surveillance videos: a survey’, Artificial Intelligence Review, Vol. 42, No. 4, pp.747-765.
[8] J. Liu, Z.J. Zha, Q.I. Tian, D. Liu, T.Yao, Q. Ling, and T. Mei, “Multi-scale triplet cnn for person re-identification”, In Proceedings of the 24th ACM international conference on Multimedia (pp. 192-196), 2016.
[9] Z. Zheng, L. Zheng, and Y. Yang, “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), 2017.
[10] D. Gray, and H. Tao, “Viewpoint invariant pedestrian recognition with an ensemble of localized characteristics”, In European conference on computer vision (pp. 262-275), 2008, Springer, Berlin, Heidelberg.
[11] C.C. Loy, C. Liu, and S. Gong, “Person re-identification by manifold ranking”, In 2013 IEEE International Conference on Image Processing (pp.
3567-3571), 2013, IEEE.
[12] D. Baltieri, R. Vezzani, and R. Cucchiara, “3dpes: 3d people database for surveillance and forensics”, In Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding (pp. 59-64), 2011.
[13] G.A. Wang, S. Yang, H. Liu, Z. Wang, Y. Yang, S. Wang, G. Yu, E. Zhou, and J. Sun, “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 (pp. 6449-6458), 2020.
[14] J. Yang, J. Zhang, F. Yu, X. Jiang, M. Zhang, X. Sun, Y.C. Chen, and W.S. Zheng, “Learning To Know Where To See: A Visibility-Aware Approach for Occluded Person Re-Identification”, In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 11885-11894), 2021.
[15] J. Miao, T. Wu, and Y. Yang, “Identifying Visible Parts via Pose Estimation for Occluded Person Re-Identification”, IEEE Transactions on Neural Networks and Learning Systems, 2021.
[16] Y. Xu, L. Zhao, and F. Qin, “Dual attention-based method for occluded person re-identification”, Knowledge-Based Systems, Vol. 212, p.106554, 2021.
[17] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778), 2016.
[18] K. Zheng, C. Lan, W. Zeng, J. Liu, Z. Zhang, and Z.J. Zha, “Pose-Guided Characteristic Learning with Knowledge Distillation for Occluded Person Re-Identification”, In Proceedings of the 29th ACM International Conference on Multimedia (pp. 4537-4545), 2021.
[19] J. Gou, B> Yu, S.J. Maybank, and D. Tao, “Knowledge distillation: A survey”, International Journal of Computer Vision, Vol. 129, No. 6, pp.1789-1819, 2021.
[20] Z. Ma, Y. Zhao, and J. Li, “Pose-guided Inter-and Intra-part Relational Transformer for Occluded Person Re-Identification”, In Proceedings of the 29th ACM International Conference on Multimedia (pp. 1487-1496), 2021.
[21] H. Wang, X. Chen, and C. Liu, “Pose-guided part matching network via shrinking and reweighting for occluded person re-identification”, Image and Vision Computing, Vol. 111, p.104186, 2021.
[22] M. Zhao, S. Zhong, X. Fu, B. Tang, and M. Pecht, “Deep residual shrinkage networks for fault diagnosis”, IEEE Transactions on Industrial Informatics, Vol. 16, No. 7, pp.4681-4690, 2019.
[23] K. Sun, B. Xiao, D. Liu, D. and J. Wang, “Deep high-resolution representation learning for human pose estimation”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5693-5703), 2019.
[24] C. Yan, G. Pang, J. Jiao, X. Bai, X. Feng, and C. Shen, “Occluded person re-identification with single-scale global representations”, In Proceedings of the IEEE/CVF International Conference on Computer
10
Vision (pp. 11875-11884), 2021.
[25] M. Jia, X. Cheng, Y. Zhai, S. Lu, S. Ma, Y. Tian, and J. Zhang, “Matching on sets: Conquer occluded person re-identification without alignment”, In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, No. 2, pp. 1673-1681, 2021.
[26] M. Jia, X. Cheng, S. Lu, and J. Zhang, “Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification”, IEEE Transactions on Multimedia, 2022.
[27] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need”, In Advances in neural information processing systems (pp. 5998-6008), 2017.
[28] Y. Li, J. He, T. Zhang, X. Liu, Y. Zhang, and F. Wu, “Diverse Part Discovery: Occluded Person Re-Identification with Part-Aware Transformer”, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2898-2907), 2021.
[29] R. Hou, B. Ma, H. Chang, X. Gu, S. Shan, and X. Chen, “Characteristic Completion for Occluded Person Re-Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[30] G. Wang, X. Chen, J. Gao, X. Zhou, and S. Ge, “Self-Guided Body Part Alignment With Relation Transformers for Occluded Person Re-Identification”, IEEE Signal Processing Letters, Vol. 28, pp.1155-1159, 2021.
[31] H. Jin, S. Lai, and X. Qian, “Occlusion-sensitive person re-identification via attribute-based shift attention”, IEEE Transactions on Circuits and Systems for Video Technology, 2021.
[32] P. Chen, W. Liu, P. Dai, J. Liu, Q. Ye, M. Xu, Q.A. Chen, and R. Ji, “Occlude them all: Occlusion-aware attention network for occluded person re-id”, In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 11833-11842), 2021.
[33] Z. Zhang, C. Lan, W. Zeng, X. Jin, and Z. Chen, “Relation-aware global attention for person re-identification”, In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 3186-3195), 2020.
[34] W. Huang, S. Liu, R. Luo, T. Si, and Z. Zhang, “Dynamically occluded samples via adversarial learning for person re-identification in sensor networks”, Ad Hoc Networks, Vol. 110, p.102316, 2021.
[35] S. Zhang, D. Chen, J. Yang, and B. Schiele, “Guided Attention in CNNs for Occluded Pedestrian Detection and Re-identification”, International Journal of Computer Vision, Vol. 129, No. 6, pp.1875-1892, 2021.
[36] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks”, Advances in neural information processing systems, Vol. 28, 2015.
[37] J. Miao, Y. Wu, P. Liu, Y. Ding, and Y. Yang, “Pose-guided characteristic alignment for occluded person re-identification”, In Proceedings of the IEEE/CVF
international conference on computer vision (pp. 542-551), 2019.
[38] J. Zhuo, Z. Chen, J. Lai, and G. Wang, “Occluded person re-identification”, In 2018 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6), 2018, IEEE.
[39] W.S. Zheng, X. Li, T. Xiang, S. Liao, J. Lai, and S. Gong, “Partial person re-identification”, In Proceedings of the IEEE International Conference on Computer Vision (pp. 4678-4686), 2015.
[40] W.S. Zheng, S. Gong, and T. Xiang, “Person re-identification by probabilistic relative distance comparison”, In CVPR 2011 (pp. 649-656), 2011, IEEE.
[41] L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable person re-identification: A benchmark”, In Proceedings of the IEEE international conference on computer vision (pp. 1116-1124), 2015.
[42] L. He, J. Liang, H. Li, and Z. Sun, “Deep spatial characteristic reconstruction for partial person re-identification: Alignment-free approach”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7073-7082), 2018.
[43] Z. Mortezaie, and H. Hassanpour, “A Survey ON AGE-INVARIANT FACE RECOGNITION METHODS”, Jordanian Journal of Computers and Information Technology (JJCIT), Vol. 5, No. 2, pp.87-96, 2019.
[44] Z. Mortezaie, H. Hassanpour, and A. Beghdadi, “A Color-Based Re-Ranking Process for People Re-Identification”, In 2021 9th European Workshop on Visual Information Processing (EUVIP) (pp. 1-5), 2021, IEEE.