journal article·2024Open access

DAUNet: Detail-Aware U-Shaped Network for 2D Human Pose Estimation

Xi Li, Yuxin Li, Zhenhua Xiao, Zhenghua Huang, Lianying Zou

Computers, Materials & Continua· Volume 81· Issue 2

Abstract

Human pose estimation is a critical research area in the field of computer vision, playing a significant role in applications such as human-computer interaction, behavior analysis, and action recognition. In this paper, we pr... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Human Pose and Action Recognition · Gait Recognition and Analysis · Hand Gesture Recognition Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (43)

Show source-provided references
1. A. Bulat and G. Tzimiropoulos, “Human pose estimation via convolutional part heatmap regression,” presented at the 14th Eur. Conf. Comput. Vis. (ECCV), Amsterdam, The Netherlands, Oct. 11–14, 2016.10.1007/978-3-319-46478-7_44 ↗
2. J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” presented at the 27th Adv. Neural Inf. Process. Syst. (NeurIPS), Montreal, QC, Canada, Dec. 8–13, 2014.
3. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” presented at the 18th Int. Conf. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), Munich, Germany, Oct. 5–9, 2015.10.1007/978-3-319-24574-4_28 ↗
4. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 26–30, 2016, pp. 770–778.10.1109/CVPR.2016.90 ↗
5. Top-heavy CapsNets based on spatiotemporal non-local for action recognition10.62411/jcta.10551 ↗
6. A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” presented at the 14th Eur. Conf. Comput. Vis. (ECCV), Amsterdam, The Netherlands, Oct. 11–14, 2016, pp. 483–499.10.1007/978-3-319-46484-8_29 ↗
7. Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. Yu and J. Sun, “Cascaded pyramid network for multi-person pose estimation,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, UT, USA, Jun. 18–22, 2018, pp. 7103–7112.10.1109/CVPR.2018.00742 ↗
8. Global relation reasoning graph convolutional networks for human pose estimation10.1109/ACCESS.2020.2973039 ↗
9. NAS-FPN: Learning scalable feature pyramid architecture for object detection
10. K. Sun, B. Xiao, D. Liu, and J. Wang, “Deep high-resolution representation learning for human pose estimation,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Long Beach, CA, USA, Jun. 16–20, 2019, pp. 5686–5696.10.1109/CVPR.2019.00584 ↗
11. W. Li et al., “Rethinking on multi-stage networks for human pose estimation,” 2019, arXiv:1901.00148.
12. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
13. Human pose regression by combining indirect part detection and contextual information10.1016/j.cag.2019.09.002 ↗
14. K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” presented at the IEEE Int. Conf. Comput. Vis. (ICCV), Venice, Italy, Oct. 22–29, 2017, pp. 2961–2969.10.1109/ICCV.2017.322 ↗
15. Faster R-CNN: Towards real-time object detection with region proposal networks10.1109/TPAMI.2016.2577031 ↗
16. An enhanced real-time human pose estimation method based on modified YOLOv8 framework
17. Human pose estimation based on frequency domain and attention module10.1016/j.neucom.2024.128318 ↗
18. YH-Pose: Human pose estimation in complex coal mine scenarios10.1016/j.engappai.2023.107338 ↗
19. Z. Cao, T. Simon, S. -E. Wei, and Y. Sheikh, “Realtime multi-person 2D pose estimation using part affinity fields,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, Jul. 21–26, 2017, pp. 7291–7299.10.1109/CVPR.2017.143 ↗
20. G. Papandreou, T. Zhu, L. -C. Chen, S. Gidaris, J. Tompson and K. Murphy, “Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model,” presented at the Eur. Conf. Comput. Vis. (ECCV), Munich, Germany, Sep. 8–14, 2018, pp. 269–286.10.1007/978-3-030-01264-9_17 ↗
21. S. Kreiss, L. Bertoni, and A. Alahi, “PifPaf: Composite fields for human pose estimation,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Long Beach, CA, USA, Jun. 16–20, 2019, pp. 11977–11986.10.1109/CVPR.2019.01225 ↗
22. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
23. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, UT, USA, Jun. 18–22, 2018, pp. 7132–7141.
24. Y. Cao, J. Xu, S. Lin, F. Wei, and H. Hu, “Non-local networks meet squeeze-excitation networks and beyond,” presented at the IEEE/CVF Int. Conf. Comput. Vis. Workshops (ICCV Workshops), Seoul, Republic of Korea, Oct. 27–28, 2019, pp. 12–13.10.1109/ICCVW.2019.00246 ↗
25. X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Long Beach, CA, USA, Jun. 16–20, 2019, pp. 510–519.10.1109/CVPR.2019.00060 ↗
26. J. Park, S. Woo, J. -Y. Lee, and I. S. Kweon, “BAM: Bottleneck attention module,” 2018, arXiv:1807.065144.
27. S. Woo, J. Park, J. -Y. Lee, and I. S. Kweon, “CBAM: Convolutional block attention module,” presented at the Eur. Conf. Comput. Vis. (ECCV), Munich, Germany, Sep. 8–14, 2018, pp. 3–19.10.1007/978-3-030-01234-2_1 ↗
28. B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose estimation and tracking,” presented at the Eur. Conf. Comput. Vis. (ECCV), Munich, Germany, Sep. 8–14, 2018, pp. 466–481.
29. C. Szegedy et al., “Going deeper with convolutions,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Boston, MA, USA, Jun. 7–12, 2015, pp. 1–9.10.1109/CVPR.2015.7298594 ↗
30. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, HI, USA, Jul. 21–26, 2017, pp. 4700–4708.10.1109/CVPR.2017.243 ↗
31. J. Chen et al., “Run, don’t walk: Chasing higher FLOPS for faster neural networks,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Vancouver, BC, Canada, Jun. 18–24, 2023, pp. 12021–12031.10.1109/CVPR52729.2023.01157 ↗
32. Q. Hou, L. Zhang, M. -M. Cheng, and J. Feng, “Strip pooling: Rethinking spatial pooling for scene parsing,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Seattle, WA, USA, Jun. 13–19, 2020, pp. 4003–4012.10.1109/CVPR42600.2020.00406 ↗
33. Y. Wu and K. He, “Group normalization,” presented at the Eur. Conf. Comput. Vis. (ECCV), Munich, Germany, Sep. 8–14, 2018, pp. 3–19.10.1007/978-3-030-01261-8_1 ↗
34. ImageNet large scale visual recognition challenge10.1007/s11263-015-0816-y ↗
35. M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, “2D human pose estimation: New benchmark and state of the art analysis,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Columbus, OH, USA, Jun. 23–28, 2014, pp. 3686–3693.10.1109/CVPR.2014.471 ↗
36. T. -Y. Lin et al., “Common objects in context,” presented at the Eur. Conf. Comput. Vis. (ECCV), Zurich, Switzerland, Sep. 6–12, 2014, pp. 740–755.10.1007/978-3-319-10602-1_48 ↗
37. Y. Li et al., “TokenPose: Learning keypoint tokens for human pose estimation,” presented at the IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Montreal, QC, Canada, Oct. 11–17, 2021, pp. 11313–11322.
38. Knowledge-guided deep fractal neural networks for human pose estimation10.1109/TMM.2017.2762010 ↗
39. H. -S. Fang et al., “AlphaPose: Whole-body regional multi-person pose estimation and tracking in real-time,” presented at the IEEE/CVF Conf. Comp. Vision Patt. Recogn. (CVPR), New Orleans, LA, USA, Jun. 19–24, 2022.
40. N. Xue, T. Wu, G. -S. Xia, and L. Zhang, “Learning local-global contextual adaptation for multi-person pose estimation,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), New Orleans, LA, USA, Jun. 19–24, 2022, pp. 13065–13074.
41. Z. Geng, K. Sun, B. Xiao, Z. Zhang, and J. Wang, “Bottom-up human pose estimation via disentangled keypoint regression,” presented at the IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Nashville, TN, USA, Jun. 19–25, 2021, pp. 14676–14686.10.1109/CVPR46437.2021.01444 ↗
42. Y. Dang, J. Yin, L. Liu, Y. Sun, Y. Hu and P. Ding, “DHRNet: A dual-path hierarchical relation network for multi-person pose estimation,” 2024, arXiv:2404.14025.10.1016/j.knosys.2024.112263 ↗
43. BCCLR: A skeleton-based action recognition with graph convolutional network combining behavior dependence and context clues