Lightweight and High-Resolution Human Pose Estimation Method

Hanbing Qu; Zhentang Jia

doi:10.3788/LOP202259.1810012

[1] Vuletic T, Duffy A, Hay L et al. Systematic literature review of hand gestures used in human computer interaction interfaces[J]. International Journal of Human-Computer Studies, 129, 74-94(2019).

[2] Xu Zhijing, Wang Dong. Multi-Pose Face Recognition with Two-Cycle Generative Adversarial Network[J]. Acta Optica Sinica, 40, 1910002(2020).

[3] Zhao X C, Hu A M, He W. Fall detection based on convolutional neural network and XGBoost[J]. Laser & Optoelectronics Progress, 57, 161024(2020).

[4] Fischler M A, Elschlager R A. The representation and matching of pictorial structures[J]. IEEE Transactions on Computers, 22, 67-92(1973).

[5] Wang L, Hu W M, Tan T N. A survey of visual analysis of human motion[J]. Chinese Journal of Computers, 25, 225-237(2002).

[6] Wei S H, Ramakrishna V, Kanade T et al. Convolutional pose machines[C], 4724-4732(2016).

[7] Lu J, Yang T F, Zhao B et al. Review of deep learning-based human pose estimation[J]. Laser & Optoelectronics Progress, 58, 2400005(2021).

[8] Newell A, Yang K Y, Deng J. Stacked hourglass networks for human pose estimation[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9912, 483-499(2016).

[9] Deng Y N, Luo J X, Jin F L. Overview of human pose estimation methods based on deep learning[J]. Computer Engineering and Applications, 55, 22-42(2019).

[10] Yang W, Li S, Ouyang W L et al. Learning feature Pyramids for human pose estimation[C], 1290-1299(2017).

[11] Cao Z, Simon T, Wei S H et al. Realtime multi-person 2D pose estimation using part affinity fields[C], 1302-1310(2017).

[12] Chen Y L, Wang Z C, Peng Y X et al. Cascaded pyramid network for multi-person pose estimation[C], 7103-7112(2018).

[13] Sun K, Xiao B, Liu D et al. Deep high-resolution representation learning for human pose estimation[C], 5686-5696(2019).

[14] Cheng B W, Xiao B, Wang J D et al. HigherHRNet: scale-aware representation learning for bottom-up human pose estimation[C], 5385-5394(2020).

[15] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[16] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

[17] Vinyals O, Toshev A, Bengio S et al. Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 652-663(2017).

[18] Molchanov P, Tyree S, Karras T et al. Pruning convolutional neural networks for resource efficient inference[EB/OL]. https://arxiv.org/abs/1611.06440

[19] Yang Y, Ramanan D. Articulated pose estimation with flexible mixtures-of-parts[C], 1385-1392(2011).

[20] Xiao B, Wu H P, Wei Y C. Simple baselines for human pose estimation and tracking[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11210, 472-487(2018).

[21] Insafutdinov E, Pishchulin L, Andres B et al. DeeperCut: a deeper, stronger, and faster multi-person pose estimation model[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9910, 34-50(2016).