Abstract
Human pose estimation is a fundamental yet challenging task in computer vision. Human pose estimation from a single image is a challenging problem due to the limited information of 2D images and the large variations in configuration and appearance of body parts. Recent works has largely improved the result of human pose estimation because of the development of convolutional neural network. However, there still exists many difficult cases, such as occluded keypoints, complex background and scale variations of human body keypoints, which cannot be well dealt with. In this paper, we design a novel scale-aware network with attentional selection that extracts multi-scale semantic information and meaningful features. Specifically, we propose a Feature Pyramid Supervision Module (FPSM), which can improve the estimation accuracy of scale variations. Meanwhile, a Spatial and Channel Attention Module (SCAM) is designed for recalibrating the spatial and channel features. Based on the proposed algorithm, we achieve state-of-the-art result on LSP dataset and make competitive performance on MPII Human Pose dataset.
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References
Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks (2013)
Wei, S., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines (2016)
Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 717–732. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_44
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Chou, C.J., Chien, J.T., Chen, H.T.: Self adversarial training for human pose estimation (2017)
Ke, L., Chang, M.C., Qi, H., Lyu, S.: Multi-scale structure-aware network for human pose estimation (2018)
Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: 2017 Computer Vision and Pattern Recognition (2014)
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2d human pose estimation: new benchmark and state of the art analysis. In: Computer Vision and Pattern Recognition (2014)
Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: British Machine Vision Conference (2010)
Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 34–50. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_3
Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: Computer Vision and Pattern Recognition (2017)
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields (2016)
Tsung, Y.L., Dollar, P., Girshick, R., He, K., Belongie, S.: Feature pyramid networks for object detection (2016)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)
Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks (2018)
Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation (2018)
Fu, J., Liu, J., Tian, H., Fang, Z., Lu, H.: Dual attention network for scene segmentation (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26–31 (2012)
Ke, S., Lan, C., Xing, J., Zeng, W., Dong, L., Wang, J.: Human pose estimation using global and local normalization. In: IEEE International Conference on Computer Vision (2017)
Tang, W., Yu, P., Wu, Y.: Deeply learned compositional models for human pose estimation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 190–206 (2018)
Acknowledgement
This work is supported by National Natural Science Foundation of China (Grant No. 61871046).
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Lv, T., Wu, L., Zhou, J., Liao, Z., Zhai, X. (2021). Scale-Aware Network with Attentional Selection for Human Pose Estimation. In: Zu, Q., Tang, Y., Mladenović, V. (eds) Human Centered Computing. HCC 2020. Lecture Notes in Computer Science(), vol 12634. Springer, Cham. https://doi.org/10.1007/978-3-030-70626-5_35
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