Current - Issue

1. Li et al. (2023) – Improved YOLOv7 for Occluded Pedestrian Detection https://www.mdpi.com/1424-8220/23/13/5912
2. Ye and Wang (2023) – RT-YOLO: Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
https://www.mdpi.com/2076-3417/13/21/12032
3. Y. Zhang, H. He, J. Li, Y. Li, J. See, and W. Lin, “Variational Pedestrian Detection,” Proc. IEEE/CVF Conf. Computer Vision and Pattern
Recognition (CVPR), pp. 11622–11631, 2021.
https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Variational_Pedestrian_Detection_CVPR_2021_paper.html
4. M. Lin, C. Li, X. Bu, M. Sun, C. Lin, J. Yan, W. Ouyang, and Z. Deng, “DETR for Crowd Pedestrian Detection,” arXiv preprint
arXiv:2012.06785, 2020 https://arxiv.org/abs/2012.06785
5. Liu and Ma (2024) – Key Points and Visible Part Fusion Attention Network https://journal.hep.com.cn/ol/EN/10.1007/s11801-024-4053-x
6. Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows,” Proc. IEEE/CVF International Conference on
Computer Vision (ICCV), pp. 10012–10022, 2021.
https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windo
ws_ICCV_2021_paper.html
7. T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature Pyramid Networks for Object Detection,” Proc. IEEE
Conf. Computer Vision and Pattern Recognition (CVPR), pp. 2117–2125, 2017.
https://openaccess.thecvf.com/content_cvpr_2017/html/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.html
8. N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-NMS—Improving Object Detection With One Line of Code,” Proc. IEEE
International Conference on Computer Vision (ICCV), pp. 5562–5570, 2017.
https://openaccess.thecvf.com/content_iccv_2017/html/Bodla_Soft-NMS_--_Improving_ICCV_2017_paper.html
9. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Proc. IEEE Conf.
Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016.
https://openaccess.thecvf.com/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html
10. J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR),
pp. 7263–7271, 2017.
https://openaccess.thecvf.com/content_cvpr_2017/html/Redmon_YOLO9000_Better_Faster_CVPR_2017_paper.html
11. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017. https://ieeexplore.ieee.org/document/7485869
12. [W. Liu et al., “SSD: Single Shot MultiBox Detector,” Proc. European Conf. Computer Vision (ECCV), pp. 21–37, 2016.
https://link.springer.com/chapter/10.1007/978-3-319-46448-0_2
13. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object
Detectors,” arXiv preprint arXiv: 2207.02696, 2022. https://arxiv.org/abs/2207.02696
14. S. Zhang, R. Benenson, and B. Schiele, “CityPersons: A Diverse Dataset for Pedestrian Detection,” Proc. IEEE Conf. Computer Vision and
Pattern Recognition (CVPR), pp. 3213–3221, 2017.
https://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_CityPersons_A_Diverse_CVPR_2017_paper.html
15. S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, “Occlusion-Aware R-CNN: Detecting Pedestrians in a Crowd,” Proc. European Conf.
Computer Vision (ECCV), pp. 657–674, 2018. https://openaccess.thecvf.com/content_ECCV_2018/html/Shifeng_Zhang_Occlusion-
Aware_R-CNN_Detecting_ECCV_2018_paper.html
16. S. Qian, L. Chen, M. Li, and X. Wang, “WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild,” IEEE Transactions
on Multimedia, 2021. https://github.com/PeizeSun/WiderPerson
17. W. Yan, Y. Chen, X. Shen, and Z. Wang, “Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based
on ResNet,” Sensors, vol. 21, no. 6, 2021. https://www.mdpi.com/1424-8220/21/6/1963