Uneingeschränkter Zugang

A single upper limb pose estimation method based on the improved stacked hourglass network


Zitieren

Andriluka, M., Pishchulin, L., Gehler, P.V. and Schiele, B. (2014). 2D human pose estimation: New benchmark and state of the art analysis, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, pp. 3686–3693. Search in Google Scholar

Artacho, B. and Savakis, A. (2020). Unipose: Unified human pose estimation in single images and videos, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR Virtual), pp. 7035–7044, (online). Search in Google Scholar

Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L. and Wang, X. (2017). Multi-context attention for human pose estimation, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, pp. 5669–5678. Search in Google Scholar

Fan, X., Zheng, K., Lin, Y. and Wang, S. (2015). Combining local appearance and holistic view: Dual-source deep neural networks for human pose estimation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 1347–1355. Search in Google Scholar

Hu, H., Liao, Z. and Xiao, X.C. (2019). Action recognition using multiple pooling strategies of CNN features, Neural Processing Letters 50(1): 379–396.10.1007/s11063-018-9932-3 Search in Google Scholar

Hu, P. and Ramanan, D. (2015). Bottom-up and top-down reasoning with convolutional latent-variable models, ArXiv: abs/1507.05699. Search in Google Scholar

Li, C., Yung, N.H.C., Sun, X. and Lam, E.Y. (2017). Human arm pose modeling with learned features using joint convolutional neural network, Machine Vision and Applications 28(1–2): 1–14.10.1007/s00138-016-0796-0 Search in Google Scholar

Lifshitz, I., Fetaya, E. and Ullman, S. (2016). Human pose estimation using deep consensus voting, European Conference on Computer Vision (ECCV), Amsterdam, Holland, pp. 246–260. Search in Google Scholar

Long, J., Shelhamer, E. and Darrell, T. (2015). convolutional networks for semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, pp. 3431–3440. Search in Google Scholar

Newell, A., Yang, K. and Deng, J. (2016). Stacked hourglass networks for human pose estimation, European Conference on Computer Vision (ECCV), Amsterdam, Holland, pp. 483–499. Search in Google Scholar

Ning, F., Shi, Y., Cai, M. and Xu, W. (2020). Various realization methods of machine-part classification based on deep learning, Journal of Intelligent Manufacturing 31(8): 2019–2032.10.1007/s10845-020-01550-9 Search in Google Scholar

Pfister, T., Charles, J. and Zisserman, A. (2015). Flowing ConvNets for human pose estimation in videos, 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp. 1913–1921. Search in Google Scholar

Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement, arXiv: 1804.02767. Search in Google Scholar

Sun, K., Xiao, B., Liu, D. and Wang, J. (2019). Deep high-resolution representation learning for human pose estimation, Computer Vision and Pattern Recognition (CVPR), Los Angeles, USA, pp. 5693–5703. Search in Google Scholar

Sun, X., Xiao, B., Wei, F., Liang, S. and Wei, Y. (2018). Integral human pose regression, European Conference on Computer Vision (ECCV), Munich, Germany, pp. 529–545. Search in Google Scholar

Tompson, J., Goroshin, R., Jain, A., LeCun, Y. and Bregler, C. (2015). Efficient object localization using convolutional networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 648–656. Search in Google Scholar

Toshev, A. and Szegedy, C. (2015). DeepPose: Human pose estimation via deep neural networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, pp. 1653–1660. Search in Google Scholar

Wei, S.-E., Ramakrishna, V., Kanade, T. and Sheikh, Y. (2016). Convolutional pose machines, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, pp. 4724–4732. Search in Google Scholar

Xiao, B., Wu, H. and Wei, Y. (2018). Simple baselines for human pose estimation and tracking, European Conference on Computer Vision (ECCV), Munich, Germany, pp. 466–481. Search in Google Scholar

Yang, W., Li, S., Ouyang, W., Li, H. and Wang, X. (2017). Learning feature pyramids for human pose estimation, 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 1281–1290. Search in Google Scholar

Yang, W., Ouyang, W., Li, H. and Wang, X. (2016). End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose estimation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, pp. 3073–3082. Search in Google Scholar

Zhang, F., Zhu, X. and Ye, M. (2019). Fast human pose estimation, Compter Vision and Pattern Recognition (CVPR), Los Angeles, USA, pp. 3517–3526. Search in Google Scholar

Zhou, J., Liu, J. and Zhang, M. (2020). Curve skeleton extraction via k-nearest-neighbors based contraction, International Journal of Applied Mathematics and Computer Science 30(1): 123–132, DOI: 10.34768/amcs-2020-0010. Search in Google Scholar

Zlatanski, M., Sommer, P., Zurfluh, F., Zadeh, S.G., Faraone, A. and Perera, N. (2019). Machine perception platform for safe human-robot collaboration, 2019 IEEE SENSORS, Montreal, Canada, pp. 1–4. Search in Google Scholar

eISSN:
2083-8492
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Mathematik, Angewandte Mathematik