Chinese Optics Letters, Volume. 23, Issue 9, 091101(2025)

Image-free cross-species pose estimation via an ultra-low sampling rate single-pixel camera

Xin Wu1, Cheng Zhou1、*, Binyu Li2, Jipeng Huang1、**, Yanli Meng1, Lijun Song3、***, and Shensheng Han4
Author Affiliations
  • 1School of Physics, Northeast Normal University, Changchun 130024, China
  • 2Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China
  • 3Changchun Institute of Technology, Changchun 130012, China
  • 4Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • show less
    References(54)

    [4] J. Cao, H. Tang, H. S. Fang et al. Cross-domain adaptation for animal pose estimation. Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 9497(2019).

    [10] M. Kresovic, T. Nguyen, M. Ullah et al. PigPose: A real-time framework for farm animal pose estimation and tracking. Proc. IFIP Int. Conf. Artif. Intell. Appl. Innov., 204(2022).

    [22] J. Deng, W. Dong, R. Socher et al. ImageNet: A large-scale hierarchical image database. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 248(2009).

    [27] K. Yan, F. Wang, B. Qian et al. Person-in-WiFi 3D: End-to-end multi-person 3D pose estimation with Wi-Fi. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 969(2024).

    [48] J. Li, S. Bian, A. Zeng et al. Human pose regression with residual log-likelihood estimation. Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 11005(2021).

    [49] K. Sun, B. Xiao, D. Liu et al. Deep high-resolution representation learning for human pose estimation. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 5686(2019).

    [50] K. He, X. Chen, S. Xie et al. Masked autoencoders are scalable vision learners. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 15979(2022).

    [51] A. Mathis, T. Biasi, S. Schneider et al. Pretraining boosts out-of-domain robustness for pose estimation. Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), 1858(2021).

    [52] B. Xiao, H. Wu, Y. Wei. Simple baselines for human pose estimation and tracking. Proc. Eur. Conf. Comput. Vis. (ECCV), 472(2018).

    [53] A. Toshev, C. Szegedy. DeepPose: Human pose estimation via deep neural networks. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 1653(2014).

    [54] K. He, X. Zhang, S. Ren et al. Deep residual learning for image recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 770(2016).

    Tools

    Get Citation

    Copy Citation Text

    Xin Wu, Cheng Zhou, Binyu Li, Jipeng Huang, Yanli Meng, Lijun Song, Shensheng Han, "Image-free cross-species pose estimation via an ultra-low sampling rate single-pixel camera," Chin. Opt. Lett. 23, 091101 (2025)

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Imaging Systems and Image Processing

    Received: Apr. 1, 2025

    Accepted: May. 9, 2025

    Published Online: Aug. 22, 2025

    The Author Email: Cheng Zhou (zhoucheng91210@163.com), Jipeng Huang (huangjp848@nenu.edu.cn), Lijun Song (ccdxslj@126.com)

    DOI:10.3788/COL202523.091101

    CSTR:32184.14.COL202523.091101

    Topics